Category: Research & Development

Innovation: Mobile-phone GPS antennas
Can They Be Better?

By Tony Haddrell, Marino Phocas, and Nico Ricquier

We examine the antenna designs that provide GPS functionality to mobile phones and why most phones still do not provide GPS operation indoors. We also see what it will take to make them better.

INNOVATION INSIGHTS by Richard Langley

WHAT ARE THREE THINGS THAT MATTER MOST for a good GPS signal? Antenna, antenna, antenna. The familiar real-estate adage can be rephrased for this purpose, although the original — location, location, location — is valid here, too.

GPS satellite signals are notoriously weak compared to familiar terrestrial signals such as those of broadcast stations or mobile-phone towers. However, if an appropriate antenna has a clear line-of-sight to the satellite, excellent receiver performance is the norm. But what constitutes an appropriate antenna? The GPS signals are right-hand circularly polarized (RHCP) to provide fade-free reception as the satellite’s orientation changes during a pass. A receiving antenna with matching polarization will transfer the most signal power to the receiver. Microstrip patch antennas and quadrifilar helices, two RHCP antennas commonly used for GPS reception, have omnidirectional (in azimuth) gain patterns with typical unamplified boresight gains of a few dB greater than that of an ideal isotropic RHCP antenna.

But what happens when signals are obstructed by trees or buildings or, worse yet, when we move indoors? Received signal strength plummets. A conventional receiver, even with a good antenna, will then have difficulty acquiring and tracking the signals, resulting in missed or even no position fixes. However, thanks in large part to massive parallel correlation, receivers have been developed with 1,000 times more sensitivity than conventional receivers, permitting operation in restricted environments, albeit usually with reduced positioning accuracy. But such operation requires a standard antenna.

So, do the GPS receivers in our mobile phones now work everywhere? Sadly, no. Consumers demand that their phones not only provide voice communications and GPS but also Bluetooth connectivity to headsets, Wi-Fi, and even an FM transmitter, all in a small form factor at reasonable cost. This requires miniaturizing the GPS antenna and possibly integrating it with the other radio services on the platform. Such compromises can, if the designer is not careful, significantly reduce receiver effectiveness with dramatically reduced antenna gain and distorted antenna patterns. This month we look at some antenna designs providing GPS functionality to mobile phones and examine why most phones still do not provide GPS operation indoors or in other challenging environments. We also find out what it will take to make them better.

“Innovation” is a regular column that features discussions about recent advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering at the University of New Brunswick, who welcomes your comments and topic ideas.

GPS is becoming a must-have feature in mobile phones, with major manufacturers launching new designs regularly, and second-tier manufacturers rapidly catching up. A quick test of any early GPS-equipped phone shows that although the incumbent GPS chip (or chipset) has high sensitivity, the integrated end result cannot perform in low signal conditions. Several challenges facing the phone designer are responsible for this, with the main two being the antenna performance and interference in the GPS band generated within the phone platform itself.

Here we explore the antenna’s role in determining overall performance of the GPS function in a mobile phone, and the potential for avoiding some platform jamming signals by choice of antenna technology.We present some results from an ongoing company study, as part of our remit to assist customers at the system integration level in support of GPS chip sales.

Many handset makers are not GPS or even RF experts, and rely on catalog components to provide their GPS and antenna hardware. Often unsuitable antennas are chosen, or the antennas are integrated in such a way that the original operation mode does not work. Study of a number of candidate phones has shown that, due to the small ground plane available, the antenna component may be merely a band-tuning device, with the ground plane contributing the signal collection function.

At the beginning of 2008, our team launched a project to understand and prioritize the problems for handset makers in the antenna area, and to provide better solutions than those currently in use.

The handset designer faces several problems when incorporating a GPS antenna. First, it has to be very low cost (a few cents, probably). Secondly, it has to be broadly omnidirectional, since there is no knowledge of “up” on a mobile phone, although some manufacturers rely on the fact that location will only be needed when the phone is in the user’s hand or an in-car holder. From the GPS receiver point of view, we would like the antenna to be as far from the communications (transmitting) antenna as possible, and also removed from other transmitting services such as Bluetooth, Wi-Fi, and FM. Users must not be able to detune the antenna out of band by placing their hands on the phone, or by raising the phone to their ears. In a perfect world, they would not obscure an antenna either.

Of course, we would also like to remove some of that platform interference at the antenna stage, and techniques such as differential RF inputs (with a differential antenna) have been proposed in the search for better noise-cancellation performance.

All of this leaves the handset designer with an impossible task, since he has run out of space to fit a decent GPS antenna with all the isolation requirements, and we typically measure GPS antennas that average 26 to 215 dB of gain with respect to a reference dipole, which measures around 21 dB compared to an isotropic antenna when integrated in the handset. Given that a 2 dB loss equates to double the time to fix (in low signal environments) or, alternately, double the amount of baseband signal-search hardware in the GPS chip, it follows that we must exert some effort to help handset integrators implement better antennas. In this respect, some larger manufacturers have in-house projects running, but smaller ones do not have antenna design teams and rely on their suppliers to provide solutions.

So, we start with cataloging the requirements, and given that most current implementations are only in the “mediocre to terrible” class, we look at ways of improving things accordingly. Of course, there are good GPS antenna solutions out there, but handset designers have mostly shunned them on the grounds of cost or even size. Restrictions on these parameters severely hamper the antenna designer, as reducing a GPS L1 antenna below its “natural” size  — about 4 centimeters for a monopole on commonly used FR4-type printed circuit board (PCB) material — inevitably means either using some higher dielectric material, which adds cost, or folding the structure up, which decreases performance.

Single-ended antennas, such as monopoles and microstrip patches, rely on a ground plane, which in a handset is undersized anyway, and is usually difficult to identify and model. True differential designs (such as a dipole) overcome this problem, but are automatically larger. As handsets get smaller and encompass more “connectivity”
(that is, more radio links, including GPS) and competition for antenna space increases, combined antennas become attractive, as they would at least help with the size issue. However, the isolation problems are increased, and since our various radios all (currently) need individual RF inputs, some new layer of complexity and filtering is needed between antenna and chip.

Theory, Performance. We undertook some practical experiments to get a feel for the gap between an antenna’s theoretical performance and its installed performance when integrated with the other phone functions. At present, the idea of modeling all the radiation interactions and mechanical arrangements within such a platform is beyond the scope of the available tools, and so practical measurements are really our only choice in the quest for better antennas.

Finally, we provide some insight into the future, given the rapid advancements driven by mobile-phone technology and the advent of the low-cost handset for new emerging markets. New challenges loom ahead for GNSS antennas, not the least being more bandwidth and multiple frequencies, and we look briefly at what must be done to keep up with handset manufacturers’ requirements in this regard.

Size of the Problem

Location-based services in mobile phones is now an expected function by the more discerning user. With more than 500 million users of such services expected by 2011, pressure on manufacturers to provide ever better user experiences and competition between phone manufacturers will bring pressure on the GPS industry for improved performance. GNSS is now the location technology of choice for mobile phones and will remain so provided that the industry can maintain leadership in cost, size, and performance. FIGURE 1 shows the expected penetration of GNSS (mostly just GPS) in the next few years.

Figure 1. GNSS penetration, mobile phones (Image: Tony Haddrell, Marino Phocas, and Nico Ricquier)

With this many users, the market will soon decide whether the performance is up to expectation or not; this in itself will determine GPS penetration going forward.

Vanishing Space. The first challenge facing the RF antenna designer working on a mobile phone is the size of the whole platform. As the size of the average phone continues to fall, manufacturers are understandably reluctant to increase size again to add new features, such as GPS. Consider the wavelengths of a phone’s various RF services. If the corresponding antennas were implemented as dipoles, the antennas would be bigger than the phone. Clearly the competition for antenna space is high. The designer will want to separate the antennas as much as possible to reduce coupling between them, both in the sense of coupling interference from one service to another (known as isolation) and in the sense of spoiling the pattern (or field) of one antenna with another (interaction).

The chip business addresses the space issue through the advent of combination or combo chips, containing such peripheral services as FM (both receive and transmit), Bluetooth, GPS, and Wi-Fi. While helping with space constraints, this development brings new challenges as these radios have to cohabit the same silicon and still perform individually, whatever the other radios are doing (transmitting music to the car radio using FM while navigating with GPS, for example). It follows that combo antennas similarly save space, but since this might involve simultaneous transmit and GPS receive functions, it is very difficult to achieve the necessary isolation, especially if the user’s body can change the coupling between functions.

FIGURE 2 shows a modern phone with some antennas identified. Not shown is the FM transmit antenna on the rear (the receive function uses the headset cable). One commercially available combo antenna and two custom-made antennas are designed to fit the mechanical layout of the phone. The GPS antenna has been placed at the top of the phone, relegating the communications antenna (really another combo since it handles four frequency bands) to the bottom of the phone, where it is subject to detuning by the user’s hand. The GPS antenna is of the PIFA (planar inverted F antenna) type, working against the ground plane of the main PCB, and is printed on a plastic molding that also implements a loudspeaker and its electrical connections.

Figure 2. Antennas in a mobile phone: 1. GSM/WCDMA antenna, 2.Wi-Fi/Bluetooth combined ceramic chip antenna, 3. GPS antenna (Image: Tony Haddrell, Marino Phocas, and Nico Ricquier)

Size. Until now, we have not looked at the size of GPS antennas. We know that a dipole (on FR4 PCB material) is about 8 centimeters in length, just a little shorter than the average phone platform. Changing to a monopole halves the natural length, but requires an “infinite” ground plane to work against. Ignoring this requirement, some manufacturers simply print a monopole on the main PCB, and put up with the coupling, losses, and pattern deficiencies that arise. Some while ago, we measured the gain of such an arrangement at about 212 dB relative to the reference dipole. So designers have turned to size-reduced antennas, either by using higher dielectric materials to form them, or by using complex shape and feed derivatives (such as the PIFA in Figure 2.)

Another combo idea is to use the communications antenna. In the case shown in FIGURE 3, this is a whip-type antenna on a clamshell-type phone. Although the antenna is free for GPS and uses no additional space, the components to tune the whip for GPS and prevent the transmit bands reaching the GPS low noise amplifier (LNA) add both cost and size. So this is not really too attractive, especially when measurements show a 216 dB performance relative to our dipole, along with a poor coverage pattern. In this model, removing the whip and leaving the ferrule to which it connects provided a 6 dB improvement in performance (for GPS only; obviously it spoils the communications function).

Figure 3. Whip antenna combination (Image: Tony Haddrell, Marino Phocas, and Nico Ricquier)

A more conventional approach is to fit an off-the-shelf GPS antenna. The problem here is that any component-type antenna will have been tested with some standardized ground plane, and most are reliant on the ground plane for both tuning, and pattern and gain. A truly balanced design avoids this problem; FIGURE 4 shows an example. Although these antennas have found favor in personal navigation devices for their superior performance, they are not usually considered for mobile phones because of cost and size considerations. This antenna did, however, give us a reference device against which we could make comparative measurements when undertaking the practical test campaign.

Figure 4. Sarantel miniature volute antenna (Image: Tony Haddrell, Marino Phocas, and Nico Ricquier)

A more usual selection is the patch type, long standard in the GPS industry. One such installation is shown in FIGURES 5 and 6, which offer two views of the same stripped-down phone. The main drawback of this arrangement is the lack of a ground plane visible to the patch antenna, giving both tuning and gain/pattern problems. We measured the gain of this antenna at about 28 dB compared to a dipole antenna connected to the same point in the circuit, which is actually at the better end of the performance range that we see. The designers gave the antenna a position at the top of the phone, as in the Figure 2 phone, but it is still squeezed for space onto the edge of the PCB in favor of the phone’s speakers and the camera components. In this phone, the communications antenna is again at the bottom of the PCB.

Figure 5. Phone with GPS patch antenna at edge of PCB (Image: Tony Haddrell, Marino Phocas, and Nico Ricquier)

Figure 6. Edge view of GPS antenna, top of phone removed. This phone includes an external GPS antenna input connector seen here mounted below the patch antenna. (Image: Tony Haddrell, Marino Phocas, and Nico Ricquier)

Interference and Isolation. The related characteristics of interference and isolation are difficult to specify and model, leading to practical measurements as the only way of accurately characterizing them. Of course, since the mechanical arrangement (including plastics, screen, battery, and PCB components) plays such a large part in determining the levels of interference and isolation, these tests can only be carried out once the phone is at the prototype stage, when major surgery to improve any particular aspect is not really an option. This also creates a problem when considering new approaches, as the result may not resemble the stand-alone tests, unless the antenna element chosen really has no significant interaction with the rest of the phone.

Most interference we see in mobile phones gets into the GPS receiver at the antenna. Typically this is followed by an RF filter of some sort, which although it spoils the noise figure, does eliminate the out-of-band transmissions from the other radios on the platform. Usually we see a plethora of self-generated in-band signals that have entered the GPS receiver via the antenna. Although we can’t filter them out, we can reduce the coupling between antenna and source as much as possible. One effect seen in current offerings is that the GPS antenna may actually be much better at coupling to interferers than it is at extracting GPS signals from free space, thus making the problem worse.

To get a view of the coupling between antennas, we tested a few available phone types to see what was the actual coupling in the antenna band of interest (see TABLE 1). Of course, one advantage of a poor antenna is that its coupling is likely to be less to adjacent antennas. Coupling is also seriously affected by the user holding the phone or the surface on which it is placed. Phones in a pocket seem to be more affected in this way. The table shows measurements with the phone assembled as completely as possible (we have to get connectivity at the antennas) but not being affected by a user or the phone’s environment.

Table: Tony Haddrell, Marino Phocas, and Nico Ricquier

Requirements

To develop requirements for a better antenna implementation, we need to consider the factors discussed above, and to develop numerical specifications against each. Given the variables involving user interaction, mechanical changes from model to model, use cases and the ever-increasing pressure on cost and size, this is far from straightforward. Our team has spent considerable time defining requirements, and a short synopsis is reported here.

In addition to the coexistence requirements (see the next section), the antenna should fulfill the following criteria:
- Minimum cost. The antenna should be of low implementation cost, preferably printed and not requiring complex connectivity to the main PCB, or to require any setup and/or tuning in production;
- Low loss. The GPS industry is used to antennas delivering around 0–3 dB (isotropic) in an upper hemispheric direction. We believe this will not be attainable in a mobile phone, but we set the gain target at an aggressive -4 dB (isotropic);
- Detuning. The antenna must continue to perform to specification with any reasonable detuning environment (such as user handling, pocket, and metal surfaces);
- Mechanical arrangement. The antenna should be of minimum dimensions that can fit the phone mechanics. For example, long and thin may be acceptable along one side of the phone. Also placement near the GPS chip avoids lossy RF tracking;
- Gain pattern. Essentially omnidirectional, accepting that other parts of the phone may cause localized dips in the pattern.
Coexistence and Cohabitation. Initially we aim to define the parameters affecting interaction with other services on the phone platform. By coexistence, we mean the ability to share a platform with the other radios and antennas and only be marginally affected by them, whatever they are doing (such as transmitting full power, low power, or idling, and with any frequency choice). This produces a straightforward immunity table (see TABLE 2) once we have determined the basic isolation between all of the elements. For the purposes of Table 2, we have chosen 15 dB as the minimum isolation value between any two antennas. Obviously there are similar tables for the other functions (GSM, 3G, Wi-Fi, Bluetooth, FM) as well.

Table: Tony Haddrell, Marino Phocas, and Nico Ricquier

A glance at Table 2 will tell the reader that the modern mobile phone implements a vast number of transmit and receive frequencies, modulation types, and standards. Of particular concern to the GPS designer is the advent of wideband CDMA signals, which can cause intermodulation products to appear in band at the intermediate frequency of the GPS receiver. Special receiver techniques are required in this case, but the antenna is unable to help except by being of naturally narrow bandwidth.

Cohabitation is a newer concept that describes the isolation between functions of the same device. In this respect, we are investigating GPS antennas combined with Wi-Fi and Bluetooth services. This is a fairly natural development, since these functions are all add-ons to a conventional phone platform, and there is a space-saving advantage in the combination. Since Wi-Fi and Bluetooth share the same band at 2.4 GHz, they have arrangements internally that allow them to coexist or choose which service is to be used if a clash is inevitable.

As a precursor to forming some specifications, our team measured a commercially available combined antenna, and TABLE 3 shows the isolation results.

Table: Tony Haddrell, Marino Phocas, and Nico Ricquier

The table highlights the need to measure antennas on a representative PCB, since other coupling factors reduce the specified isolation by >6 dB compared to the manufacturer’s reference setup, where the part is the only component on the demonstration board.

Real-Life Testing

A number of tests were carried out on available solutions to gain some information and experience about current offerings and platforms.

At one of our facilities, we have a GTEM (gigahertz transverse electromagnetic) cell, which was constructed in house and has been verified to be working properly (see FIGURE 7). A GTEM cell is an expanded transmission line within which a uniform electromagnetic field can be generated for determining antenna properties such as gain and bandwidth. The internal space at the septum (40 centimeters) is big enough to handle antenna sizes used by GPS. It has a small side door and some feedthroughs (coaxial) to the bottom plate. The RF foam absorbers used inside the GTEM work well at 1.5 GHz (the cell can work from 100 MHz to above 10 GHz).

Figure 7. The GTEM cell and related test equipment (Photo: Tony Haddrell, Marino Phocas, and Nico Ricquier)

Differential vs. Single-Ended Antennas. The first test conducted concerned comparison of balanced and unbalanced antennas, the theory being that a balanced antenna would help with interference because it would be presented to the GPS receiver as a common mode signal (that is, balanced on the positive and negative inputs). The NXP GNS7560 single-chip GPS solution is configurable for single or differential input to the LNA, and was used to conduct the tests.

The trial began with calibration of the test setup using the balanced antenna shown in Figure 4, against which we measured a printed dipole antenna and a monopole equivalent, arranged to incorporate a balun to make it of the same size as the dipole (see FIGURE 8). Once this calibration had been made, we sought to generate an interfering signal on the GPS receiver test board so that comparisons of interference rejection could be made. This was done in two different ways, in case the method of exciting the GPS board was subject to resonances or peculiar standing-wave modes. First, we injected an RF interferer into the power supply via the USB cable that was both powering the GPS board and the communications link to it. The jamming created in this manner was increased until a predetermined drop in GPS sensitivity was reached. A number of frequencies were tried and the results compared. In the second setup, we directly applied an RF signal across the ground plane of the GPS board, using a coaxial feed to excite the ground plane, and repeated the stages described above.

Figure 8. Antennas used in the balanced vs. unbalanced antenna testing (Photo: Tony Haddrell, Marino Phocas, and Nico Ricquier)

Results for both tests were within 2 dB of each other, and showed that the differential approach could reduce local jammer pickup by only 4–6 dB. This is probably due to the differential structure being of similar size to the test platform (chosen to be similar to a phone platform), and therefore not achieving true differential coupling to the on-board radiated jammer. With this marginal advantage, we concluded that the benefit was barely justified by the extra complexity and size involved in differential antennas. Note that this conclusion may be different for smaller (for example, high dielectric) differential antennas, although these are currently not available. We are resolved to revisit this possibility at a later date.

Testing Some Commercial Parts. Having elected to continue in unbalanced-only mode, we tested some commercially available antenna components, which are all aimed at mobile phones and span a range of technologies. Each antenna was tested on its recommended reference design without other mobile phone components or features. However, we did use phone-sized boards, representative plastics, and a real user’s hand in these tests. TABLE 4 shows the comparative results.

Table: Tony Haddrell, Marino Phocas, and Nico Ricquier

For return loss measurements we used a vector network analyzer and a ferrite absorber clamp to suppress cable common-mode effects. For measuring the antenna-received voltage, we used an open-air setup with a horn antenna placed 1 meter away from the DUT (device under test) antenna. The horn is fed with a 100 dBuV 1575 MHz CW signal and the received signal at the DUT is inspected with a spectrum analyzer. The horn is mounted so that we have vertical polarization. Initially, we were only concerned with looking for the maximum attainable voltage and we have positioned the DUT also to vertical polarization. Wooden tables were used to avoid reflections. The last two columns in Table 4 are with plastic in close proximity to the antenna element and the last column is with the plastic grabbed by the hand (as one would grab a phone).

The first thing to note is that of the antennas reported above (which were the best of a bigger number of test pieces) the performance is roughly the same for all of them when configured in their reference mechanical arrangement and not interacting with the phone environment. From the table, we can see that for the particular antenna tested in two positions, its location on the ground plane defines its performance (the ceramic-loaded antenna lost 3 dB in voltage terms when moved to the shorter side of the board). This may be a problem in that the best position performance-wise is not the best for the case where the user interacts with the complete assembly. Also, we see that the user and the plastics have a big effect. In short, the component-type antennas currently available don’t show exciting performance in a real environment, but most are competent GPS antennas when integrated according to their makers’ instructions. However, this is often not possible due to mechanical and other constraints. One drawback of the monopole type of device is its need for a ground-plane-free area underneath the component, and this often conflicts with the requirements of the other antennas, which are looking to maximize the ground plane in the phone.

Novel Approaches, Validation

We started this program to identify the requirements of a good GPS antenna, test some theories and current components, and then develop a new approach. From the foregoing, it is clear that a design that is part of the phone mechanics itself will be better integrated and more predictable in the final implementation. Our design team has begun to model and test some more PCB-centric solutions that attempt to mimic at least the current performance of commercial components, and to minimize the amount of ground-plane loss. We do all our testing on representative (in size and conductivity) phone PCBs. A new approach to thinking about potential arrangements is to use the previously mentioned concept that the whole board is the radiator and the antenna is actually a tuning and feed device. One promising possibility is a slot antenna (or slot feed) formed by removing a small notch of ground plane along the top edge of the phone PCB. Some phones have demonstrated success in forming Bluetooth antennas in this manner, although the lower frequency of GPS does not help.

On a separate path, another idea is to print a PIFA (or similar structure) on the plastics themselves and have it work against the phone ground plane in total. In this case, it is relatively easy to get good performance, but connection of the feed to the main board (where the GPS chipset will be located) is a non-trivial mechanical problem.

Testing of some candidate solutions is under way, and we expect reference designs for customer use to be the deliverable from this work. In addition, it is clear that there is not a one-solution-fits-all conclusion, and that more work will be necessary as phone and GPS designs are further developed.

Acknowledgments

The authors thank the antenna engineering team at NXP’s Mobile and Personal Innovation Center, especially Tony Kerselaers, Felix Elsen, and Norbert Philips who conducted the trials reported here. This article is based on the paper “A New Approach to Cellphone GPS Antennas” presented at ION GNSS 2008.

TONY HADDRELL is a fellow staff architect.

ST-Ericsson in Daventry, England, and a director of iNS Ltd., Weedon, England.

MARINO PHOCAS is an RF systems engineer with ST-Ericsson.

NICO RICQUIER heads the Connectivity Group at NXP Semiconductors in Leuven, Belgium.

Some Mobile Phone Terms

Bluetooth (BT). A communications protocol operating in the 2.4 GHz Industrial, Scientific and Medical (ISM) frequency band, enabling electronic devices to connect and communicate in short-range ad hoc networks.

CDMA. Code division multiple access is a channel access method used by some mobile-phone carriers that allows multiple users to share the same radio frequencies using spread spectrum signals.

DCS1800. Digital Cellular Service version of GSM operating in the 1700 and 1800 MHz bands.

EDGE. Enhanced Data Rates for GSM Evolution, a third-generation (3G) version of GSM.

EGSM900. The Extended GSM 900 MHz band.

FDD. Frequency-division duplexing, a communications protocol that uses different carrier frequencies for transmitt
ing and receiving.

FM. The broadcast frequency modulation band.

GMSK. Gaussian minimum shift keying, a continuous-phase frequency-shift keying modulation scheme used for GSM communications.

GSM. Global System for Mobile communications, the most popular mobile phone standard.

GSM850. A GSM version operating in the 800 MHz band.

PCS1900. Personal Communications Service version of GSM operating in the 1800 and 1900 MHz bands.

QPSK. Quadrature phase-shift keying. A modulation technique used in CDMA systems.

Triplexer. A filtering device to provide isolation between communications and GPS circuits when sharing an antenna.

W-CDMA. Wideband CDMA, an enhanced, 3G version of CDMA.

Wi-Fi 802.11b/g. Wi-Fi describes a standard class of wireless local area network (WLAN) protocols based on the IEEE 802.11 standards operating primarily in the 2.4 GHz band.

FURTHER READING

• Mobile Phone Development

“The Smartphone Revolution” by F. van Diggelen in GPS World, Vol. 20, No. 12, December 2009, pp. 36–40.

• Signal Compatibility Issues

“Jammers – the Enemy Inside!” by M. Phocas, J. Bickerstaff, and T. Haddrell in Proceedings of ION GNSS 2004, the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation, Long Beach, California, September 21–24, 2004, pp. 156–165.

• High Sensitivity GPS Receiver

“A Single Die GPS, with Indoor Sensitivity – the NXP GNS7560” by T. Haddrell, J.P. Bickerstaff, and M. Conta in Proceedings of ION GNSS 2008, the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 16–19, 2009, pp. 1201–1209.

• Mobile Phone GPS Antennas

“A Compact Broadband Planar Antenna for GPS, DCS-1800, IMT-2000, and WLAN Applications” by R. Li, B. Pan, J. Laskar, M.M. Tentzeris in IEEE Antennas and Wireless Propagation Letters, Vol. 6, 2007, pp. 25–27 (doi:10.1109/LAWP.2006.890754).

“Getting into Pockets and Purses: Antenna Counters Sensitivity Loss in Consumer Devices” by B. Hurte and O. Leisten in GPS World, Vol. 16, No. 11, November 2005, pp. 34–38.

“Miniature Built-in Multiband Antennas for Mobile Handsets” by Y.X. Guo, M.Y.W. Chia, and Z.N. Chen in IEEE Transactions on Antennas and Propagation, Vol. 52, No. 8, August 2004, pp. 1936–1944 (doi: 10.1109/TAP.2004.832375).

“Mobile Handset System Performance Comparison of a Linearly Polarized GPS Internal Antenna with a Circularly Polarized Antenna” by V. Pathak, S. Thornwall, M. Krier, S. Rowson, G. Poilasne, L. Desclos in Proceedings of IEEE Antennas and Propagation Society International Symposium 2003, Columbus, Ohio, June 22-27, 2003, Vol. 3, pp. 666–669 (doi:10.1109/APS.2003.1219935).

Planar Antennas for Wireless Communications by K.L. Wong, published by John Wiley & Sons, New York, 2003.

• Basics of GPS Antennas

“GNSS Antennas: An Introduction to Bandwidth, Gain Pattern, Polarization, and All That” by G.J.K. Moernaut and D. Orban in GPS World, Vol. 20, No. 2, February 2009, pp. 42–48.

“A Primer on GPS Antennas” by R.B. Langley in GPS World, Vol. 9, No. 7, July 1998, pp. 50–54.
February 1, 2010
Innovation: Collective Detection

Enhancing GNSS Receiver Sensitivity by Combining Signals from Multiple Satellites

By Penina Axelrad, James Donna, Megan Mitchell, and Shan Mohiuddin

A new approach to enhancing signal sensitivity combines the received signal power from multiple satellites in a direct-to-navigation solution.

INNOVATION INSIGHTS by Richard Langley

ALTHOUGH I HAVE MANAGED the Innovation column continuously since GPS World’s first issue, it wasn’t until the second issue that I authored a column article. That article, co-written with Alfred Kleusberg, was titled “The Limitations of GPS.” It discussed some of the then-current problems of GPS, including poor signal reception, loss of signal integrity, and limited positioning accuracy.

In the ensuing 20 years, both signal integrity and positioning accuracy have improved significantly. Advances in the GPS control segment’s capabilities to continuously monitor and assess signal performance, together with receiver-autonomous integrity monitoring and integrity enhancement provided by augmentation systems, have reduced worries about loss of signal integrity. The removal of Selective Availability and use of error corrections provided by augmentation systems, among other approaches, have improved positioning accuracy.

But the problem of poor reception due to weak signals is still with us. In that March/April 1990 article, we wrote “[GPS] signals propagate from the satellites to the receiver antenna along the line of sight and cannot penetrate water, soil, walls, or other obstacles very well. … In surface navigation and positioning applications, the signal can be obstructed by trees, buildings, and bridges. … [In] the inner city streets of urban areas lined with skyscrapers, the ‘visibility’ of the GPS satellites is very limited. In such areas, the signals can be obstructed for extended periods of time or even [be] continuously unavailable.”

Poor signal reception in other than open-sky environments is still a problem with conventional GPS receivers. However, extending signal integration times and using assisted-GPS techniques can give GPS some degree of capability to operate indoors and in other restricted environments, albeit typically with reduced positioning accuracy. An antenna with sufficient gain is needed and capable systems are available on the market. The pilot channels of modernized GNSS signals will also benefit signal acquisition and tracking in challenging environments.

In this month’s column, we look at a completely different approach to enhancing signal sensitivity. Rather than requiring each satellite’s signal to be acquired and tracked before it can be used in the navigation solution, the new approach — dubbed “collective detection” — combines the received signal power from multiple satellites in a direct-to-navigation-solution procedure. Besides providing a quick coarse position solution with weak signals, this approach can be used to monitor the signal environment, aid deeply-coupled GPS/inertial navigation, and assist with terrain and feature recognition.

“Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick.

Growing interest in navigating indoors and in challenging urban environments is motivating research on techniques for weak GPS signal acquisition and tracking. The standard approach to increasing acquisition and tracking sensitivity is to lengthen the coherent integration times, which can be accomplished by using the pilot channels in the modernized GPS signals or by using assisted GPS (A-GPS) techniques. These techniques operate in the traditional framework of independent signal detection, which requires a weak signal to be acquired and tracked before it is useful for navigation. This article explores a complementary, but fundamentally different, approach that enhances signal sensitivity by combining the received power from multiple GPS satellites in a direct-to-navigation-solution algorithm. As will be discussed in the following sections, this collective detection approach has the advantage of incorporating into the navigation solution information from signals that are too weak to be acquired and tracked, and it does so with a modest amount of computation and with no required hardware changes. This technology is appropriate for any application that requires a navigation solution in a signal environment that challenges traditional acquisition techniques. Collective detection could be used to monitor the signal environment, aid deeply coupled GPS/INS during long outages, and help initiate landmark recognition in an urban environment. These examples are explained further in a subsequent section. In order to understand how the collective detection algorithm works, it is instructive to first consider the traditional approach to acquisition and tracking.

Acquisition Theory and Methods

In a typical stand-alone receiver, the acquisition algorithm assesses the signal’s correlation power in discrete bins on a grid of code delay and Doppler frequency (shift). The correlation calculations take the sampled signal from the receiver’s RF front end, mix it with a family of receiver-generated replica signals that span the grid, and sum that product to produce in-phase (I) and quadrature (Q) correlation output. The correlation power is the sum of the I and Q components, I2 + Q2. Plotting the power as a function of delay and frequency shift produces a correlogram, as shown in FIGURE 1. It should be noted that both correlation power and its square root, the correlation amplitude, are found in the GPS literature. For clarity, we will always use the correlation power to describe signal and noise values.

If a sufficiently powerful signal is present, a distinct peak appears in the correlogram bin that corresponds to the GPS signal’s code delay and Doppler frequency. If the peak power exceeds a predefined threshold based on the integration times and the expected carrier-to-noise spectral density, the signal is detected. The code delay and Doppler frequency for the peak are then passed to the tracking loops, which produce more precise measurements of delay — pseudoranges — from which the receiver’s navigation solution is calculated. When the satellite signal is attenuated, however, perhaps due to foliage or building materials, the correlation peak cannot be distinguished and the conventional approach to acquisition fails.

The sensitivity of traditional tracking algorithms is similarly limited by the restrictive practice of treating each signal independently. More advanced tracking algorithms, such as vector delay lock loops or deeply integrated filters, couple the receiver’s tracking algorithms and its navigation solution in order to take advantage of the measurement redundancy and to leverage information gained from tracking strong signals to track weak signals. The combined satellite detection approach presented in this article extends the concept of coupling to acquisition by combining the detection and navigation algorithms into one step.

Collective Detection

In the collective detection algorithm, a receiver position and clock offset grid is mapped to the individual GPS signal correlations, and the combined correlation power is evaluated on that grid instead of on the conventional independent code delay and Doppler frequency grids. The assessment of the correlation power on the position and clock offset grid leads directly to the navigation solution. The mapping, which is key to the approach, requires the receiver to have reasonably good a priori knowledge of its position, velocity, and clock offset; the GPS ephemerides; and, if necessary, a simplified ionosphere model. Given this knowledge, the algorithm defines the position and clock offset search grid centered on the assumed receiver state and generates predicted ranges and Doppler frequencies for each GPS signal, as illustrated in FIGURE 2. The mapping then relates each one of the position and clock offset grid points to a specific code delay and Doppler frequency for each GPS satellite, as illustrated in FIGURE 3. Aggregating the multiple delay/Doppler search spaces onto a single position/clock offset search space through the mapping allows the navigation algorithm to consider the total correlation power of all the signals simultaneously. The correlation power is summed over all the GPS satellites at each position/clock-offset grid point to create a position domain correlogram. The best position and clock-offset estimates are taken as the grid point that has the highest combined correlation power. This approach has the advantage of incorporating into the position/clock-offset estimate information contained in weak signals that may be undetectable individually using traditional acquisition/tracking techniques.

It should be noted that a reasonable a priori receiver state estimate restricts the size of the position and clock-offset grid such that a linear mapping, based on the standard measurement sensitivity matrix used in GPS positioning, from the individual signal correlations, is reasonable. Also, rather than attempt to align the satellite correlations precisely enough to perform coherent sums, noncoherent sums of the individual satellite correlations are used. This seems reasonable, given the uncertainties in ranging biases between satellites, differences and variability of the signal paths through the ionosphere and neutral atmosphere, and the large number of phases that would have to be aligned.

Applications

The most obvious application for collective detection is enabling a navigation fix in circumstances where degraded signals cause traditional acquisition to fail. The sweet spot of collective detection is providing a rapid but coarse position solution in a weak signal environment. The solution can be found in less time because information is evaluated cohesively across satellites. This is especially clear when the algorithm is compared to computationally intensive long integration techniques.

There are several ways that collective detection can support urban navigation. This capability benefits long endurance users who desire a moderate accuracy periodic fix for monitoring purposes. In some circumstances, the user may wish to initiate traditional tracking loops for a refined position estimate. However, if the signal environment is unfavorable at the time, this operation will waste valuable power. The collective detection response indicates the nature of the current signal environment, such as indoors or outdoors, and can inform the decision of whether to spend the power to transition to full GPS capabilities.

In urban applications, deeply integrated GPS/INS solutions tolerate GPS outages by design. However, if the outage duration is too long, the estimate uncertainty will eventually become too large to allow conclusive signal detection to be restored. Running collective detection as a background process could keep deeply integrated filters centered even in long periods of signal degradation. Because collective detection approaches the acquisition problem from a position space instead of the individual satellite line-of-sight space, it provides inherent integrity protection. In the traditional approach, acquiring a multipath signal will pollute the overall position fix. In collective detection, such signals are naturally exposed as inconsistent with the position estimate.

Another use would be to initialize landmark correlation algorithms in vision navigation. Landmark correlation associates street-level video with 3D urban models as an alternative to (GPS) absolute position and orientation updates. This technique associates landmarks observed from ground-level imagery with a database of landmarks extracted from overhead-derived 3D urban models. Having a coarse position (about 100 meters accuracy) enhances initialization and restart of the landmark correlation process. Draper Laboratory is planning to demonstrate the utility of using collective detection to enable and enhance landmark correlation techniques for urban navigation.

In all of these applications, collective detection is straightforward to implement because it simply uses the output of correlation functions already performed on GPS receivers.

Simulations and Processing

The new algorithm has been tested using live-sky and simulated data collected by a Draper Laboratory wideband data recorder. A hardware GPS signal simulator was used to simulate a stationary observer receiving 11 equally powered GPS signals that were broadcast from the satellite geometry shown in FIGURE 4. The data recorder and the signal simulator were set up in a locked-clock configuration with all of the simulator’s modeled errors set to zero. No frequency offsets should exist between the satellites and the receiver. A clock bias, however, does exist because of cable and other fixed delays between the two units. The data recorder houses a four-channel, 14-bit A/D module. It can support sample rates up to 100 MHz. For this work, it was configured to downconvert the signal to an IF of 420 kHz and to produce in-phase and quadrature samples at 10 MHz.

Results and Discussion

To combine satellites, a position domain search space is established, centered on the correct location and receiver clock bias. A grid spacing of 30 meters over a range of ± 900 meters in north and east directions, and ± 300 meters in the vertical. In the first simulated example, the correlation power for all the satellites is summed on the position grid using a single 1-millisecond integration period. In this case, the true carrier-to-noise-density ratio for each signal is 40 dB-Hz. The results are shown in FIGURE 5. The plots in the left panel show the individual signal correlations as a function of range error. The four plots in the upper-right panel show several views of the combined correlation as a function of position error. The upper-left plot in the panel shows the correlation value as a function of the magnitude of the position error. The upper-right plot shows the correlation as a function of the north-east error, the lower-left the north-down error, and the lower-right the east-down error. Notice how the shape of the constant power contours resembles the shape of the constant probability contours that would result from a least-squares solution’s covariance matrix. The final plot, the bottom-right panel, shows a 3D image of the correlation power as a function of the north-east error. It is clear in these images that in the 40 dB-Hz case each satellite individually reaches the highest correlation power in the correct bin and that the combined result also peaks in the correct bin. In the combined satellite results, each individual satellite’s correlation power enters the correlogram as the ridge that runs in a direction perpendicular to the receiver-satellite line-of-sight vector and represents a line of constant pseudorange.

FIGURE 6 shows a similar set of graphs for a simulator run at 20 dB-Hz. The plots in the left panel and the four plots in the upper-right panel show the individual and combined correlations, as in Figure 5. In the lower-right panel, the 3D image has been replaced with correlations calculated using 20 noncoherent 1-millisecond accumulations. The indistinct peaks in many of the individual correlations (left panel) suggest that these signals may not be acquired and tracked using traditional methods. Those signals, therefore, would not contribute to the navigation solution. Yet in the combined case, those indistinct peaks tend to add up and contribute to the navigation solution. These results indicate the feasibility of using the information in weak signals that may not be detectable using traditional methods and short acquisition times. The situation is further improved by increasing the number of noncoherent integration periods.

Impact of Reduced Geometry. Of course, it is a bit unrealistic to have 11 satellites available, particularly in restricted environments, so we also considered three subsets of four-satellite acquisitions, under the same signal levels. FIGURE 7 compares the position domain correlograms for the following 20 dB-Hz cases: (1) a good geometry case (PRNs 3, 14, 18, 26), (2) an urban canyon case where only the highest 4 satellites are visible (PRNs 15, 18, 21, 22), and (3) a weak geometry case where just a narrow wedge of visibility is available (PRNs 18, 21, 26, 29). As expected, the correlation power peak becomes less distinct as the satellite geometry deteriorates. The pattern of degradation, morphing from a distinct peak to a ridge, reveals that the position solution remains well constrained in some directions, but becomes poorly constrained in others. Again, this result is expected and is consistent with the behavior of conventional positioning techniques under similar conditions.

Focusing on Clock Errors. In some real-world situations, for example, a situation where a receiver is operating in an urban environment, it is possible for the position to be fairly well known, but the clock offset and frequency to have substantial uncertainty. FIGURE 8 shows how the combined satellites approach can be used to improve sensitivity when viewed from the clock bias and frequency domain. The figure presents example 1-millisecond correlograms of clock bias and clock drift for three 20 dB-Hz cases: (1) a single GPS satellite case; (2) a four-satellite, good geometry case; and (3) an 11-satellite, good geometry case. The assumed position solution has been offset by a random amount (generated with a 1-sigma of 100 meters in the north and east components, and 20 meters in the up component), but no individual satellite errors are introduced. These plots clearly show the improved capability for acquisition of the clock errors through the combining process.

Live Satellite Signals. FIGURE 9 shows combined correlograms derived from real data recorded using an outdoor antenna. The first example includes high-signal-level satellites with 1.5-second noncoherent integration. The second example includes extremely attenuated satellite signals with a long noncoherent integration period of six seconds.

The plots in the upper-left and upper-right panels show combined correlograms as a function of the north-east position error for satellite signals with carrier-to-noise-density ratios of 48 dB-Hz or higher. The plots in the lower-left and lower-right panels show combined correlograms resulting from much weaker satellites with carrier-to-noise-density ratios of roughly 15 to 19 dB-Hz, using a coherent integration interval of 20 milliseconds and a noncoherent interval of six seconds. FIGURE 10 shows one of the individual single-satellite correlograms. In this attenuated case, the individual satellite power levels are just barely high enough to make them individually detectable. This is the situation in which collective detection is most valuable.

Conclusions

The example results from a hardware signal simulator and live satellites show how the noncoherent combination of multiple satellite signals improves the GPS position error in cases where some of the signals are too weak to be acquired and tracked by traditional methods. This capability is particularly useful to a user who benefits from a rapid, but coarse, position solution in a weak signal environment. It may be used to monitor the quality of the signal environment, to aid deeply coupled navigation, and to initiate landmark recognition techniques in urban canyons. The approach does require that the user have some a priori information, such as a reasonable estimate of the receiver’s location and fairly accurate knowledge of the GPS ephemerides. Degradation in performance should be expected if the errors in these models are large enough to produce pseudorange prediction errors that are a significant fraction of a C/A-code chip. Absent that issue, the combined acquisition does not add significant complexity compared to the traditional approach to data processing. It can be used to enhance performance of existing acquisition techniques either by improving sensitivity for the current noncoherent integration times or by reducing the required integration time for a given sensitivity. Further development and testing is planned using multiple signals and frequencies.

Acknowledgments

The authors appreciate the contributions of David German and Avram Tewtewsky at Draper Laboratory in collecting and validating the simulator data; Samantha Krenning at the University of Colorado for assistance with the simulator data analysis and plotting; and Dennis Akos at the University of Colorado for many helpful conversations and for providing the Matlab software-defined radio code that was used for setting up the acquisition routines. This article is based on the paper “Enhancing GNSS Acquisition by Combining Signals from Multiple Channels and Satellites” presented at ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, held in Savannah, Georgia, September 22–25, 2009.

The work reported in the article was funded by the Charles Stark Draper Laboratory Internal Research and Development program.

Manufacturers

Data for the analyses was obtained using a Spirent Federal Systems GSS7700 GPS signal simulator and a GE Fanuc Intelligent Platforms ICS-554 A/D module.

PENINA AXELRAD is a professor of aerospace engineering sciences at the University of Colorado at Boulder. She has been involved in GPS-related research since 1986 and is a fellow of The Institute of Navigation and the American Institute of Aeronautics and Astronautics.

JAMES DONNA is a distinguished member of the technical staff at the Charles Stark Draper Laboratory in Cambridge, Massachusetts, where he has worked since 1980. His interests include GNSS navigation in weak signal environments and integrated inertial-GNSS navigation.

MEGAN MITCHELL is a senior member of the technical staff at the Charles Stark Draper Laboratory. She is involved with receiver customization for reentry applications and GPS threat detection.

SHAN MOHIUDDIN is a senior member of the technical staff at the Charles Stark Draper Laboratory. His interests include GNSS technology, estimation theory, and navigation algorithms.

FURTHER READING

• Background
“Noncoherent Integrations for GNSS Detection: Analysis and Comparisons” by D. Borio and D. Akos in IEEE Transactions on Aerospace and Electronic Systems, Vol. 45, No. 1, January 2009, pp. 360–375 (doi: 10.1109/TAES.2009.4805285).

“Impact of GPS Acquisition Strategy on Decision Probabilities” by D. Borio, L. Camoriano, and L. Lo Presti in IEEE Transactions on Aerospace and Electronic Systems, Vol. 44, No. 3, July 2008, pp. 996–1011 (doi:10.1109/TAES.2008.4655359).

“Understanding the Indoor GPS Signal” by T. Haddrell and A.R. Pratt in Proceedings of ION GPS 2001, the 14th International Technical Meeting of the Satellite Division of The Institute of Navigation, Salt Lake City, Utah, September 11–14, 2001, pp. 1487–1499.

“The Calculation of the Probability of Detection and the Generalized Marcum Q-Function” by D.A. Shnidman in IEEE Transactions on Information Theory, Vol. 35, No. 2, March 1989, pp. 389–400 (doi: 10.1109/18.32133).

• Weak Signal Acquisition and Tracking
“Software Receiver Strategies for the Acquisition and Re-Acquisition of Weak GPS Signals” by C. O’Driscoll, M.G. Petovello, and G. Lachapelle in Proceedings of The Institute of Navigation 2008 National Technical Meeting, San Diego, California, January 28-30, 2008, pp. 843–854.

“Deep Integration of Navigation Solution and Signal Processing” by T. Pany, R. Kaniuth, and B. Eissfeller in Proceedings of ION GNSS 2005, the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation, Long Beach, California, September 13–16, 2005, pp. 1095–1102.

“Deeply Integrated Code Tracking: Comparative Performance Analysis” by D. Gustafson and J. Dowdle in Proceedings of ION GPS 2003, the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 9–12, 2003, pp. 2553–2561.

“Block Acquisition of Weak GPS Signals in a Software Receiver” by M.L. Psiaki in Proceedings of ION GPS 2001, the 14th International Technical Meeting of the Satellite Division of The Institute of Navigation, Salt Lake City, Utah, September 11–14, 2001, pp. 2838–2850.

• General Combining Techniques
“Coherent, Non-Coherent, and Differentially Coherent Combining Techniques for the Acquisition of New Composite GNSS Signals” by D. Borio, C. O’Driscoll, and G. Lachapelle, in IEEE Transactions on Aerospace and Electronic Systems, Vol. 45, No. 3, July 2009, pp. 1227–1240.

“Comparison of L1 C/A-L2C Combined Acquisition Techniques” by C. Gernot, K. O’Keefe, and G. Lachapelle in Proceedings of the European Navigation Conference ENC-GNSS 2008, Toulouse, France, April 23–25, 2008, 9 pp.

Performance Analysis of the Parallel Acquisition of Weak GPS Signals by C. O’Driscoll, Ph.D. dissertation, National University of Ireland, Cork, 2007; available on line: <http://plan.geomatics.ucalgary.ca/papers/phd_thesis_cillian_odriscoll_jan07.pdf>.

• Coherent Combining of Signals from Multiple Satellites
“GPS PRN Code Signal Processing and Receiver Design for Simultaneous All-in-View Coherent Signal Acquisition and Navigation Solution Determination” by R. DiEsposti in Proceedings of The Institute of Navigation 2007 National Technical Meeting, San Diego, California, January 22–24, 2007, pp. 91–103.

January 1, 2010
Innovation: Improving Dilution of Precision

A Companion Measure of Systematic Effects

By Dennis Milbert

GPS receivers must deal with measurements and models that have some degree of error, which gets propagated into the position solution. If the errors are systematically different for the different simultaneous pseudoranges, as is typically the case when trying to correct for ionospheric and tropospheric effects, these errors propagate into the receiver solution in a way that is fundamentally different from the way that random errors propagate. So in addition to dilution of precision, we need a companion measure of systematic effects. In this month’s column, we introduce just such a measure.

INNOVATION INSIGHTS by Richard Langley

WE LIVE IN AN IMPERFECT WORLD. We know this all too well from life’s everyday trials and tribulations. But this statement extends to the world of GPS and other global navigation satellite systems, too. A GPS receiver computes its three-dimensional position coordinates and its clock offset from four or more simultaneous pseudoranges. These are measurements of the biased range (hence the term pseudorange) between the receiver’s antenna and the antenna of each of the satellites being tracked. The receiver processes these measurements together with a model describing the satellite orbits and clocks and other effects, such as those of the atmosphere, to determine its position. The precision and accuracy of the measured pseudoranges and the fidelity of the model determine, in part, the overall precision and accuracy of the receiver-derived coordinates.

If we lived in an ideal world, a receiver could make perfect measurements and model them exactly. Then, we would only need measurements to any four satellites to determine our position perfectly. Unfortunately, the receiver must deal with measurements and models that have some degree of error, which gets propagated into the position solution. Furthermore, the geometrical arrangement of the satellites observed by the receiver — their elevation angles and azimuths — can significantly affect the precision and accuracy of the receiver’s solution, typically degrading them. It is common to express the degradation or dilution by dilution of precision (DOP) factors. Multiplying the measurement and model uncertainty by an appropriate DOP value gives an estimate of the position error.

These estimates are reasonable if the measurement and model errors are truly random. However, it turns out that this simple geometrical relationship breaks down if some model errors are systematic. If that systematic error is a constant bias and if it is common to all pseudoranges measured simultaneously, then the receiver can easily estimate it along with its clock offset, leaving the position solution unaffected. But if the errors are systematically different for the different simultaneous pseudoranges, as is typically the case when trying to correct for ionospheric and tropospheric effects, these errors propagate into the receiver solution in a way that is fundamentally different from the way that random errors propagate. This means that in addition to DOP, we need a companion measure of systematic effects.

In this month’s column, Dennis Milbert introduces just such a measure — the error scale factor or ESF. ESF, combined with DOP, forms a hybrid error model that appears to more realistically portray the real-world GPS precisions and accuracies we actually experience.

“Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick.

The recent edition of the Standard Positioning Service (SPS) Performance Standard (PS) and the corresponding document for the Precise Positioning Service (PPS) both emphasize a key element. They only specify the GPS signal-in-space (SIS) performance. Since these standards do not define performance for any application of a GPS signal, it becomes even more important to understand the relationship of signal statistics to positioning accuracy. Historically, as well as in Appendix B of the SPS-PS and PPS-PS, this relationship is modeled by covariance elements called dilution of precision (DOP).

Many references are available which describe DOP. The core of DOP is the equation of random error propagation:

Q_x = ( A^t Q^-1A ) ^-1

where, for n observations, A is the n x 4 matrix of observation equation partial differentials, Q is the n x n covariance matrix of observations, and Q_x is the 4 x 4 covariance matrix of position and time parameters (X, Y, Z, T) used to compute DOPs. This equation describes the propagation of random error (noise) in measurements into the noise of the unknown (solved for) parameters. Elements of the Q_x matrix are then used to form the DOP.

The equation above is linear for any measurement scale factor of Q. For example, halving the dispersion of the measurements will halve the dispersion of the positional error. This scaling behavior is exploited when forming DOP where, by convention, Q is taken as the identity matrix, I. DOPs then become unitless, and are treated as multipliers that convert range error into various forms of positional error. Thus, we see relationships in the SPS-PS Appendix B such as:

UHNE = UERE x HDOP

where UERE is user equivalent range error, HDOP is horizontal dilution of precision, and UHNE is the resulting user horizontal navigation error.

DOP is a model relationship between signal statistics and position statistics based on random error propagation. But, since the cessation of Selective Availability (SA), the GPS signal in space now displays less random dispersion than the average systematic effects of ionosphere and troposphere propagation delay error. It’s useful to test if a random error model can capture the current behavior of GPS positioning on the ground.

The Federal Aviation Administration collects GPS data at the Wide Area Augmentation System (WAAS) reference stations and analyzes GPS SPS performance. These analyses are documented in a quarterly series called the Performance Analysis (PAN) Reports. To test horizontal and vertical accuracy, the 95th percentile of positional error, taken comprehensively over space and time, without any subsetting whatsoever, is chosen. This measure is always found in Figures 5-1 and 5-2 of the PAN reports. Note that the Appendix A 95% “predictable accuracy” in the reports through PAN report number 51 refers to a worst-site condition and cannot be considered comprehensive. The PAN report 95th percentiles of positional error measured since the cessation of SA are reproduced in FIGURE 1.

Figure 1. Accuracy (95th percentile) of horizontal and vertical L1-only point positioning. GPS data are gathered at WAAS reference stations, analyzed quarterly, and published in the PAN reports. The red line is vertical accuracy and the blue line is horizontal accuracy.

By the DOP error model, the positional error should be the product of the underlying pseudorange error times HDOP
or vertical DOP (VDOP). It is convenient to form the vertical to horizontal positional error ratio, V/H, shown in FIGURE 2. This error ratio should, formally, be independent of the magnitude of the range error. The error ratio should reflect the GPS constellation geometry. One expects the positional error ratio, V/H, to be relatively uniform, and it should also equal the VDOP/HDOP ratio. However, Figure 2 shows a number of spikes (from PAN Reports 37, 40, 44, 64) in the error ratio, and a general increase over the past nine years. The positional error ratios in Figure 2 do not portray the uniform behavior expected for a DOP error model based on random error propagation.

Figure 2. Ratio of the vertical/horizontal accuracy (95th percentile). The spikes indicate effects that are not caused by constellation geometry or signal-in-space error.

The PAN reports form a challenge to our ability to understand and describe the measured performance of the GPS system. In the past, when SA was imposed on the GPS signal, the measured pseudorange displayed random, albeit time-correlated, statistics. DOP was effective then in relating SA-laden range error to positional error. Now, with SA set to zero, the role of DOP should be revisited.

In this article, I will introduce a hybrid error model that takes into account not only the effects of random error but also that of systematic error due to incomplete or inaccurate modeling of observations. But first, let’s examine predicted GPS performance based on DOP calculations alone.

Random Error Propagation

FIGURE 3 displays detail of a 24-hour HDOP time series. Considerable short wavelength structure is evident. Spikes as thin as 55 seconds duration can be found at higher resolutions. Given the abrupt, second-to-second transitions in DOP, and given that the GPS satellites orbit relative to the Earth at about 4 kilometers per second, one may suspect that short spatial scales as well as short time scales are needed to describe DOP behavior.

Figure 3. All-in-view HDOP, July 20, 2007, near the Washington Monument, 5° elevation angle cutoff. Note the abrupt transitions, and that HDOP is around 1.0. VDOP (not pictured) is about 1.5.

To investigate DOP transitions, the conterminous United States (CONUS) was selected as a study area. HDOP and VDOP, with a 5° elevation-angle cutoff, were computed using an almanac on a regular 3 minute by 3 minute grid over the region 24°-53° N, 230°-294° E. These DOP grids were computed at 2,880 30-second epochs for July 20, 2007, yielding more than two trillion DOP evaluations. This fine time/space granularity was selected to capture most of the complex DOP structure seen in Figure 3.

FIGURE 4 plots the HDOP distribution over CONUS and parts of Canada and Mexico at 02:40:30 GPS Time. This epoch was selected to show an HDOP excursion (HDOP 4 2.58) seen in the red zone just north of Lake Ontario. DOPs are rather uniform within zones, and these zones have curved boundaries. The boundaries are sharply delineated and move geographically in time, which explains the jumps seen in high-rate DOP time series (as in Figure 3). The broad, curved boundaries seen in Figure 4 are the edges of the footprints of the various GPS satellites. The gradual variation in hue within a zone shows the gradual variation of DOP as the spatial mappings of the local elevation angles change for a given set of GPS satellites in a region.

Figure 4. HDOP, July 20, 2007, 02:40:30 GPS Time, 5° cutoff. The curved boundaries, which show abrupt transitions in DOP, are the edges of the footprints of various GPS satellites.

The 2,880 color images of HDOP (and VDOP) were converted into an animation that runs 4 minutes and 48 seconds at 10 frames per second. The effect is kaleidoscopic, as the various footprints cycle across one another, and as the zones change color. The footprint boundaries transit across the map in various directions and create a changing set of triangular and quadrilateral zones of fairly uniform DOP. There is no lower limit to temporal or spatial scale of a given DOP zone delimited by three transiting boundaries. The size of a zone can increase or shrink in time. Zones can take a local maximum, a local minimum, or just some intermediate DOP value. And the DOP magnitude in a given zone often changes in time. The animation shows that the DOP maximums are quite infrequent, and the DOPs generally cluster around the low end of the color scale. The animations are available.

To get a quantitative measure of distribution, the HDOPs (and VDOPs) are histogrammed with a bin width of 0.01 in FIGURE 5. Tabulations of various percentiles, computed from the bin counts, are displayed in TABLE 1. HDOP ranges from 0.600 to 2.685 and VDOP ranges from 0.806 to 3.810.

Figure 5. HDOP, July 20, 2007, 5° cutoff. DOP has a strong central tendency and a tail showing rare instances of large DOP. Here HDOP ranges from 0.600 to 2.685.

Chart: GPS World

Since the DOP zone boundaries are related to satellites rising and setting, it is natural to expect a relation to a selected cutoff limit of the elevation angle. As a test, DOP was recomputed with a 15° cutoff limit, and histogrammed with a bin width of 0.01 in FIGURE 6. Tabulations of various percentiles, computed from the bin counts, are displayed in TABLE 2. HDOP ranges from 0.735 to 26.335, and VDOP ranges from 1.045 to 72.648.

Figure 6. HDOP, July 20, 2007, 15° cutoff. DOP is sensitive to cutoff angle. Here HDOP ranges from 0.735 to 26.335. This is a large increase over the HDOP with a 5° cutoff.

The Figures 5 and 6 and Tables 1 and 2 show that DOPs are markedly sensitive to cutoff angles. The histogram tails increase and the maximum DOPs dramatically increase as the cutoff angle is increased. The 95th percentile HDOP increases by about 50 percent when the cutoff angle increases from 5° to 15°. The solutions weaken to some degree and the poorer solutions get much worse. The effect is somewhat greater for VDOP.

One normally considers DOP as a property of the satellite constellation that has a space-time mapping. DOP is seen to strongly depend upon horizon visibility. This is a completely local property that is highly variable throughout the region. Clearly, DOP depends on the antenna site as well as the constellation.

Systematic Error Propagation

It is known that certain error sources in GPS are systematic. Such errors will display different behaviors from random error. For example, the impact of ionosphere and troposphere error on GPS performance has been recognized in the literature (see “Further Reading”). DOP is not successful in modeling systematic effects. A new metric for systematic positional error is needed.

Consider a systematic bias, b, in measured pseudorange, R. One may propagate the bias through the weighted least-squares adjustment:

(A^tQ^-1A) x = A^tQ^-1y

by setting the n x 1 vector, y = b. Vector x will then contain the differential change (error) in coordinates (δx, δy, δz, δt) induced by the bias. The coordinate rror can then be transformed into the north, east, and up local horizon system (δN, δE, δU). Positional systematic error is defined as horizontal error, (δN² + δE²)^½, and vertical error, |δU|.

As with DOP, the equations above are linear for any measurement bias scale factor, k, which applies to all satellite pseudoranges at an epoch. For example, if one halves a bias that applies to all pseudoranges (for example, ky), then one will halve the associated coordinate error, kx. Analogous to DOP, we take bias with a base error b = 1, to create a unitless measure that can be treated as a multiplier. We now designate the horizontal error as horizontal error scale factor (HESF) and vertical error as vertical error scale factor (VESF). This adds a capability of developing error budgets for systematic effects that parallels DOP.

Systematic errors in GPS position solutions have a distinctly different behavior than random errors. This is illustrated by a trivial example. If one repeats any of the tests above with a constant value, c, for the bias, one will find that, aside from computer round-off error, no systematic error propagates into the position. The coordinates are recovered perfectly, and the constant bias is absorbed into the receiver time bias parameter, δ t. This is no surprise, since the GPS point position model is constructed to solve for a constant receiver clock bias.

The ionosphere and troposphere, on the other hand, cause unequal systematic errors in pseudoranges. These systematic errors are greater for lower elevation angle satellites than for higher elevation angle satellites. So, unlike the trivial example above, these errors cannot be perfectly absorbed into δ t. The systematic errors never vanish, even for satellites at zenith. One may expect some nonzero positional error that does not behave randomly.

The systematic effect of the ionosphere and troposphere differ through their mapping functions. These are functions of elevation angle, E, and are scale factors to the systematic effect at zenith (E = 90°). Because of the different altitudes of the atmospheric layers, the mapping functions take different forms. For this reason, systematic error scale factors (ESFs) for the ionosphere and troposphere must be considered separately.

Ionosphere Error Scale Factor. Following Figure 20-4 of the Navstar GPS Space Segment/Navigation User Interfaces document, IS-GPS-200D, the ionospheric mapping function associated with the broadcast navigation message, F, is

F = 1.0 + 16.0 (0.53 – E)³

where E is in semicircles and where semicircles are angular units of 180 degrees and of π radians. Since the base error is considered to be b = 1 for ESFs, y is simply populated with the various values of F appropriate to the elevation angles, E, of the various satellites visible at a given epoch. The resulting HESF and VESF values will portray how systematic ionosphere error will be magnified into positional error, just as DOPs portray how random pseudorange error is magnified into positional error.

As was done with the DOPs, more than two trillion ionosphere HESFs (and VESFs) were computed for CONUS and histogrammed in FIGURE 7. Tabulations of various percentiles, computed from the bin counts, are displayed in TABLE 3. Ionosphere HESF ranges from 0.0 to 0.440 and VESF ranges from 1.507 to 2.765.

Figure 7. HESF, ionosphere, July 20, 2007, 5° cutoff. The HESF-I are much smaller than the HDOP. The VESF-I (not depicted) have an average larger magnitude than the VDOP.

The distribution of the HESF-I in Figure 7 differs profoundly from HDOP. Ionosphere error is seen to have a weak mapping into horizontal positional error, with HESF-I values approaching zero, and having a long tail. The VESF-I is roughly comparable to the magnitude of the ionosphere mapping function at a low elevation angle. The VESFs also fall into a fixed range, without long tails, and are skewed to the right. The percentiles in Table 3 show ionosphere error has a greater influence on the height than that predicted by DOP.

Systematic Range Error and Height. Both troposphere and ionosphere propagation error leads to error in height. The mechanism underlying the behavior in Table 3 is not obvious. Consider the simplified positioning problem in FIGURE 8, where we solve for two unknowns: the up-component of position and receiver bias, dt (which includes effects common to all pseudoranges measured at the same time, such as the receiver clock offset). The atmosphere will cause the pseudoranges AO, BO, and CO to measure systematically longer. However, the ionosphere error will be about three times larger at low elevation angles than at the zenith. (Troposphere error will be about 10 times larger at low elevation angles than at the zenith.)

Figure 8. Schematic of pseudorange positioning. Computing up and receiver clock bias through 3 pseudoranges (AO, BO, CO), BO is biased by +5 meters ionosphere; AO and CO are biased by +15 meters ionosphere. Clock bias will absorb the +15 meters from the conflicting horizontal pseudoranges, and overcorrect the BO pseudorange by 10 meters.

In this simplified example, assume the zenith pseudorange, BO, measures 5 meters too long because of unmodeled ionosphere delay. Then the near-horizon pseudoranges, AO and CO, will measure 15 meters too long. AO and CO can’t both be 15 meters too long at the same time, so that bias is absorbed by the receiver bias term, dt. That dt term is also a component of the up solution from BO. While the AO and CO pseudoranges have superb geometry in establishing receiver clock bias, they also have terrible geometry in establishing height. The height is solved from the BO pseudorange that is overcorrected by 10 meters. Point O rises by 10 meters. The presence of the receiver bias term causes atmospheric systematic error to be transferred to the height. It also shows that the horizontal error will largely be canceled in mid-latitude and equatorial scenarios.

Troposphere Error Scale Factor. A variety of troposphere models and mapping functions are available in the literature. We choose the Black and Eisner mapping function, M(E), which is specified in the Minimum Operational Performance Standards for WAAS-augmented GPS operation:

As was done for the ionosphere ESFs, y is populated with the various values of M(E) for the satellites visible at a given epoch.

The troposphere HESFs (and VESFs) are computed for CONUS and histogrammed in FIGURE 9. Tabulations of various percentiles, computed from the bin counts, are displayed in TABLE 4. Troposphere HESF ranges from 0.0 to 5.203, and VESF ranges from 1.882 to 13.689.

Figure 9. HESF, troposphere, July 20, 2007, 5° cutoff. The HESF-Ts are significantly larger than the HESF-Is, showing that unmodeled troposphere propagation error can more readily influence horizontal position. The VESF-Ts are substantially larger than the VDOPs and VESF-Is.

The troposphere HESFs in Table 4 have similarities with, and differences from, the ionosphere HESFs of Table 3. Troposphere error maps more strongly into the horizontal coordinates than ionosphere error. The VESFs are much larger than the HESFs. And the VESFs still fall into a fixed range, without long tails.

Unlike DOP, which is derived from random error propagation, ESF is constructed for systematic error propagation. A good “vest pocket” number for the tropospheric delay of pseudorange at zenith is 2.4 meters at mean sea level. Thus, without a troposphere model, one can expect horizontal error of 1.80 x 2.4 meters = 4.32 meters or less 95 percent of the time according to Table 4.

Cutoff Angle. We now briefly consider the behavior of ESF under an increased elevation angle cutoff. The ionosphere ESFs with a 10° cutoff show minor improvements. This is a distinct difference from DOP (see Table 2), which showed degraded precision with a larger cutoff angle. The troposphere ESFs with a 10° cutoff angle are computed from histogram bin counts (TABLE 5). 10° cutoff troposphere HESF ranges from 0.0 to 3.228 and VESF ranges from 1.161 to 9.192.

Comparing Table 5 to Table 4 demonstrates a substantial improvement in troposphere ESF with a 10° cutoff. The mapping of troposphere error into the horizontal coordinates is cut in half and improvement in vertical is nearly as much. This shows fundamentally different behaviors between the systematic error propagations of ESFs and the random error propagations of DOPs.

GPS Error Models

We can now construct a calibrated error model derived from the PAN measurements that accommodates both random error and systematic error behaviors. To begin, consider the simple random error model (as found in Appendix B of the SPS-PS and PPS-PS):

M_h = r D_h

M_v = r D_v

where r denotes an unknown calibration coefficient for random error, and where:

D_h is HDOP 95th percentile at 5° cutoff

(1.24 by Table 1)

D_v is VDOP 95th percentile at 5° cutoff (1.92 by Table 1)

M_h is measured 95th percentile horizontal error (varies with PAN report number, Figure 1)

M_v is measured 95th percentile vertical error (varies with PAN report number, Figure 1).

One immediately sees by inspection that we have not one, but two estimates of r for each PAN report. And these estimates are inconsistent.

Now, add the ionosphere and troposphere components to produce a hybrid error model:

M_h² = r² D_h² + i² I_h² + t² T_h²

M_v² = r² D_v² + i² I_v² + t² T_v²

where i denotes an unknown calibration coefficient for residual ionosphere systematic error and where:

I_h is HESF-I 95th percentile at 5° cutoff (0.162 by Table 3)

I_v is VESF-I 95th percentile at 5° cutoff (2.40 by Table 3)

t is an unknown coefficient for residual troposphere systematic error

T_h is HESF-T 95th percentile at 5° cutoff

T_v is VESF-T 95th percentile at 5° cutoff.

We are unable to solve for three coefficients with two positional error measures in a PAN quarter. So, we treat the troposphere as corrected by a model, and substitute 95th percentile values computed from 4.9 centimeters of residual troposphere error:

M_h² = r² D_h² + i² I_h² + (0.01)2

M_v² = r² D_v² + i² I_v² + (0.60)2

This leads to a 2 x 2 linear system for each PAN quarter. The r and i coefficients are solved for and displayed in FIGURE 10.

Figure 10. Hybrid model of random and ionosphere error by PAN report number. Red line is random error; blue line is ionosphere. Gaps in the plot indicate inconsistent coefficient solutions.

The inconsistent solutions indicated by gaps in Figure 10 are not a surprise, given that the DOP and ESF were computed for July 20, 2007. Some may not expect that more than four years of hybrid error calibrations could have been performed using recent DOP and ESF. Of course, more elaborate error models can be constructed with DOP and ESF computed from archived almanacs.

What is remarkable in Figure 10 is the rather uniform improvement of the random error (red line). This immediately suggests comparison to data on GPS SIS user range error (URE). Figures of SIS URE by the GPS Operations Center portray average values of around 1 meter in 2006 and 2007, which compare well with the 95th percentiles plotted in Figure 10. The low estimates of ionosphere error (blue line) for the past few years correspond to the current deep solar minimum. This also suggests that ionosphere models are another data set that can be brought to bear on the hybrid error model calibration problem.

This hybrid error model is just a first attempt at simultaneously reconciling random and systematic effects. It shows some capability to distinguish ionosphere error from other truly random noise sources. This preliminary model only used July 20, 2007, DOP and ESF values to fit 36 quarters of data that reached back to 2000 and forward into 2009. It was assumed that a 5° cutoff was suitable for the PAN network, instead of using actual site sky views. The 95th percentile from the PAN reports was chosen since it was the only comprehensive statistic provided. A 50th percentile, if it had been available, is a more robust statistic.

Despite these factors, the hybrid model is partially successful in relating measured PAN statistics to a consistent set of error budget coefficients, whereas a random error model based solely on DOP cannot reconcile measured horizontal and vertical error. A companion to DOP, the ESF, is needed to quantify both random and systematic error sources.

Acknowledgments

Thanks go to ARINC, whose WSEM software provided reference values to test correct software operation. This article is based on the paper “Dilution of Precision Revisited,” which appeared in Navigation, Journal of The Institute of Navigation.

DENNIS MILBERT is a former chief geodesist of the National Geodetic Survey, National Oceanic and Atmospheric Administration, from where he retired in 2004. He has a Ph.D. from The Ohio State University. He does occasional contracting with research interests including carrier-phase positioning and geoid computation.

FURTHER READING

• Dilution Of Precision

“Dilution of Precision Revisited” by D. Milbert in Navigation, Journal of The Institute of Navigation, Vol. 55, No. 1, 2008, pp. 67–81.

“Dilution of Precision” by R.B. Langley in GPS World, Vol. 10, No. 5, May 1999, pp. 52–59.

“Satellite Constellation and Geometric Dilution of Precision” by J.J. Spilker Jr. and “GPS Error Analysis” by B.W. Parkinson in Global Positioning System: Theory and Applications, Vol. 1, edited by B.W. Parkinson and J.J. Spilker Jr., Progress in Astronautics and Aeronautics, Vol. 163, American Institute of Aeronautics and Astronautics, Washington, D.C., 1996, pp. 177–208 and 469–483.

• Measures of GPS Performance

Global Positioning System (GPS) Standard Positioning Service (SPS) Performance Analysis Report, No. 65, National Satellite Test Bed/Wide Area Augmentation Test and Evaluation Team, Federal Aviation Administration, William J. Hughes Technical Center, Atlantic City International Airport, New Jersey.

• Impact of Systematic Error on GPS Performance

“Post-Modernization GPS Performance Capabilities” by K.D. McDonald and C.J. Hegarty in Proceedings of the IAIN World Congress and the 56th Annual Meeting of The Institute of Navigation, San Diego, California, June 26–28, 2000, pp. 242–249.

“The Residual Tropospheric Propagation Delay: How Bad Can It Get?” by J.P. Collins and R.B. Langley in Proceedings of ION GPS-98, 11th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 15–18, 1998, pp. 729–738.

“The Role of the Clock in a GPS Receiver” by P.N. Misra in GPS World, Vol. 7, No. 4, April 1996, pp. 60–66.

“The Effects of Ionospheric Errors on Single-Frequency GPS Users” by R.L. Greenspan, A.K. Tet[e]wsky, J. I. Donna, and J.A. Klobuchar in ION GPS 1991, Proceedings of the 4th International Technical Meeting of the Satellite Division of the Institute of Navigation, Albuquerque, New Mexico, September 11–13, 1991, pp. 291–298.

• GPS Standards and Specifications

Global Positioning System Standard Positioning Service Performance Standard, U.S. Department of Defense, Washington, D.C., September 2008.

Global Positioning System Precise Positioning Service Performance Standard, U.S. Department of Defense, Washington, D.C., February 2007.

Navstar Global Positioning System Interface Specification, IS-GPS-200D, Revision D, IRN-200D-001, by ARINC Engineering Services, LLC for GPS Joint Program Office, El Segundo, California, March 2006.

November 1, 2009
Innovation: It’s Not All Bad

Understanding and Using GNSS Multipath

By Andria Bilich and Kristine M. Larson

Telltale signs of multipath are the fluctuations in the signal-to-noise ratios (SNRs) reported by some GNSS receivers. In this month’s column, the authors look at how an analysis of SNR values can be used to map the multipath environment surrounding an antenna so that models of multipath can be constructed to further minimize its effect. Also, although an annoyance for most GNSS users, it turns out that multipath has its positive points.

INNOVATION INSIGHTS by Richard Langley

CAST YOUR MIND BACK 30 OR 40 YEARS. (Sorry, students, this exercise is for the older folks.) What was one of the most striking features of the suburban landscape? Virtually every house was topped with a Yagi TV antenna. The only way to receive TV signals before cable and satellite TV was directly from the transmitter tower. And, unless you had one of those fancy antenna rotors, reception wasn’t always that great. Not only did we have to put up with weak signals, there was the problem of multipath. Besides a direct signal from the transmitter, the antenna could pick up a signal reflected off a nearby building, say, resulting in a delayed ghost image to the right of the main image on the TV screen. Even those out in the country weren’t immune from multipath as a fluttery image might be seen caused by reflections from passing aircraft.

These days, with TV signals primarily delivered by cable and satellite, we don’t see multipath much anymore. But we do hear it in our cars, from time to time, while listening to FM radio. (Students can tune back in now.) Although the FM “capture effect” provides some margin against multipath, it is not uncommon to lose stereo reception or to experience fading out of the signal while driving in built-up areas as a result of reflections.

This same multipath phenomenon also affects GNSS signals. Unlike satellite TV antennas, the antennas feeding our GNSS receivers are omnidirectional. So we have the possibility of not only receiving a direct, line-of-sight signal from a GNSS satellite but also any indirect signal from the satellite that gets reflected off nearby buildings or other objects or even the ground. GNSS antenna and receiver manufacturers have developed techniques to minimize the impact of multipath on the GNSS observables. Nevertheless, there is typically some residual multipath afflicting the pseudorange and carrier-phase observables that limits the precision and accuracy of position determinations.

Telltale signs of multipath are the quasi-periodic fluctuations in the signal-to-noise ratios (SNRs) reported by some GNSS receivers, and in this month’s column, we learn how an analysis of SNR values can be used to map and better understand the multipath environment surrounding an antenna.

And, although an annoyance for most GNSS users, it turns out that multipath is not all bad. By analyzing the SNR fluctuations due to multipath, characteristics of the reflector can be deduced. If the reflector is the ground, then the amount of moisture in the soil can be measured. GNSS for measuring soil moisture? Who would have thought?

“Innovation” is a regular column that features discussions about recent advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering at the University of New Brunswick.

We often hear “multipath” blamed as the last great source of unmodeled errors in GNSS observations, and therefore positions. But what is multipath? And what can we do about it? Can we remove multipath, or understand its temporal and spatial nature, or use it in new and novel ways? In this article, we address some of these outstanding multipath questions through the lens of the signal-to-noise ratio, or SNR. This article begins with background on the multipath phenomenon and discusses how carrier-phase multipath is related to SNR, an observable that is routinely collected by GNSS receivers but rarely used. The remainder of the article details a few new applications of SNR observations for multipath analysis. With this single observable type and a few assumptions about its relation to tracking loops and the environment surrounding the antenna, we can understand the multipath environment, remove multipath errors from carrier-phase measurements, and in some cases even transform this error into a new source of environmental information.

Multipath is exactly what it sounds like — a signal that travels along more than one path. When GNSS radio waves propagate from the GNSS satellite toward the receiving antenna, it is possible for the incoming signal to travel more than one path via reflection, diffraction, scattering, or a combination of these. Although all these phenomena contribute to multipath, in this article we limit multipath to reflections of a specular nature. Specular reflections occur when an electromagnetic wave hits an object (such as the surface of the Earth, a building, or a car) that is smooth relative to the signal wavelength. Upon reflection from the smooth surface, the outgoing energy is coherent, discrete, and sent in a single direction. From this point forward, multipath is taken to mean specular reflections from a large object.

When received by a GNSS antenna, this coherent reflected signal will disturb the tracking loops and distort the measured code and phase. The code and phase distortions occur because the GNSS receiver tracks a composite signal, which is the sum of the direct or line-of-sight signal and one or more multipath signals. The composite signal is biased from the direct signal simply because the multipath signal travels a longer path length than the desired direct signal. GNSS tracking and positioning rely upon the assumption of direct line-of-sight between satellite and receiver, thus tracking a composite signal will result in mismeasurement of the carrier and code ranges.

Why is multipath still an unsolved problem with GNSS positioning? As discussed below, multipath is a site-specific phenomenon — each GNSS site or satellite or vehicle will have a unique multipath-generating environment. Multipath is also dynamic — errors evolve with motion of the GNSS satellites and change as the reflecting surfaces (such as growing vegetation, moving cars, dry or damp ground) around the receiving antenna also change. Multipath errors cannot be simply differenced away — multipath at one station will not cancel out upon differencing with observables from another station. Nor can multipath always be “averaged out” — with real-time or rapid static GNSS positioning, the spatial and temporal complexity of site-specific multipath environments can adversely affect position determination.

Simplified Multipath Model

On the most basic level, multipath errors are driven by the geometric relationships between the receiving point (the GNSS receiver antenna), the sending point (the GNSS satellite antenna), and the reflecting object. We illustrate these geometric relationships using simple ray tracing; for a more involved ray-tracing technique, see the paper “Development and Testing of a New Ray-Tracing Approach to GNSS Carrier-Phase Multipath Modelling” listed in Further Reading. The geometric relationships between the satellite, receiving antenna, and reflecting objects dictate the additional path length traveled by the multipath signal, and how this path length changes as the satellite moves.

In an ideal, multipath-free world, this geometry is described only by the line-of-sight betwxeen satellite and receiver, which we describe via the azimuth and elevation angle of the satellite relative to the receiver. The geometry becomes more complicated when a reflecting/multipath object is introduced. TABLE 1 introduces some multipath terms and FIGURE 1 shows how these factors combine to create a forward-scatter multipath environment where a single reflected signal is received by the GNSS antenna. This illustration shows an antenna receiving two signals from one GNSS satellite, the desired direct ray and a second ray that reflects off a tilted, planar object before reception. For this example, we assume all angles are coplanar and disregard the third dimension.

FIGURE 1. (a) Forward-scatter multipath geometry, where the red arrows indicate the longer path traveled by the multipath signal relative to the direct signal. See Table 1 for definition of terms. (b) Signal amplitudes after including antenna gain pattern (green line) effects and attenuation upon reflection at a surface; see Table 2 for definition of terms.

Using the multipath terms listed in Table 1 and the geometric relationships depicted in Figure 1a, the additional distance traveled by the reflected/multipath signal relative to the direct one is the path delay. The phase of the multipath signal (again, relative to the direct signal) is the angular equivalent of path delay:

     [1]

Already, we see that the path delay and multipath relative phase are a function of the antenna-reflector distance (h) and the angle of reflection from the surface (β), and that the same multipath object will result in different multipath phases for different GNSS signals due to the dependence on λ.

As discussed below, the time-varying nature of multipath is key to understanding and mitigating its effects. Thus we examine the multipath frequency, that is, the rate of change of the multipath phase:

     [2]

If we assume a single stationary reflecting object, the only time-varying factor in Figure 1 is the satellite — as the satellite moves relative to the receiving antenna, the reflection point also moves, changing the path delay and multipath relative phase. Substituting the angular relationships (see Figure 1a) between the satellite, receiver, and reflecting object into the previous equation makes this more obvious:

[3]

But how is “multipath frequency” related to quantities measured by our GNSS receivers: the code range, carrier phase, and signal-to-noise ratio (SNR)? To answer that question, we must introduce another set of multipath quantities, which describe the dominant signal strength factors (TABLE 2) for the direct and multipath signals; we ignore thermal noise, cable losses, etc.

The amplitude of the direct signal (A_d) is equivalent to the GNSS signal strength as it is received and is affected by the antenna gain pattern (Figure 1b). The multipath signal comes through the antenna gain pattern at a different angle; by design, most GNSS antennas will apply less gain at angles consistent with common multipath geometries, such as below the antenna horizon. The multipath signal will also experience some amount of attenuation upon reflection; the combination of attenuation and antenna gain yields the amplitude of the multipath signal (A_m). Note that the broadcast GNSS signals are right-hand circularly polarized (RHCP), which are largely converted to left-hand polarization upon reflection. Thus the simplified “gain pattern” introduced here must incorporate both RHCP and LHCP patterns.

Under the simplified model of GNSS receiver response to tracking direct plus short-delay (smaller than 1.5 code chips) reflected signals, the multipath relative phase and signal amplitudes describe both the code and carrier-phase multipath errors, respectively denoted ρ_MP and δφ:

        [4]

.      [5]

These equations are derived from code and carrier tracking behavior in the presence of multipath. Look in Further Reading for precise derivations and additional background material.

In addition to carrier phase and code observables, GNSS receivers routinely record SNR (or the related carrier-to-noise-density ratio — C/N₀) for each satellite. As the term indicates, SNR is a ratio of signal power to the noise floor of the GNSS observation, and has conventionally been used only for comparison of signal strengths between channels and satellites or to assess interference. Like code and carrier-phase multipath errors, SNR is a function of multipath phase and signal strengths:

.      [6]

If we remove the effects of the direct signal, the remaining SNR is due only to multipath and is reduced to a simple function of multipath signal amplitude, relative phase, and a time-invariant phase offset:

.      [7]

Note that the equations for code multipath, carrier-phase multipath, and SNR contain the cosine or sine of the multipath relative phase, ψ. Therefore all three GNSS observables will have quasi-sinusoidal behavior driven by ω. To illustrate this, FIGURE 2 gives an example for a rising satellite reflecting off horizontal ground 1.0 meters below the antenna. All three GNSS observables oscillate at the same frequency; however, pseudorange error and SNR are in phase while carrier-phase error is 90 degrees out of phase.

FIGURE 2. Simulated carrier-phase error, code error, and SNR (recorded direct-plus-multipath SNR in green; SNR due to multipath alone in blue in linear amplitude units for a horizontal surface 1.0 meters below the antenna, assuming Rs 5 0.2 reflection coefficient and a choke ring antenna gain pattern.

In this article, we use SNR observations to understand and quantify multipath effects. We choose SNR over the other observable types because multipath effects on SNR have the most unambiguous relationship to multipath. Typical levels of pseudorange noise will swamp all but the most extreme of multipath errors; carrier-phase data are more precise, but extracting multipath from these data requires first modeling clocks, orbits, and atmospheric delays. SNR data are directly related to carrier-phase multipath, are largely independent of the above effects, and are determined independently for individual satellites. Unfortunately, not all GNSS receivers provide SNR data with the requisite precision and accuracy to clearly observe the multipath relationships; see “Scientific Utility of the Signal-to-Noise Ratio (SNR) Reported by Geodetic GPS Receivers” in Further Reading for information on high-utility SNR. When SNR data are of sufficient quality, they can provide a unique and direct window on the multipath errors affecting the code and carrier observations.

SNR Multipath Applications

A number of new scientific applications of SNR data are evolving to exploit the above multipath relationships. In the following sections, we describe three different SNR-multipath applications and provide relevant (although not exhaustive) references. All of these applications draw upon the above relationships and require precise and accurate SNR data that conform to the simplified multipath model described above.

Multipath Corrections. Recall that the multipath errors in GNSS observables are simply a function of signal amplitudes and the relative phase between direct and multipath signals. It stands to reason that if these amplitudes and phases can be estimated, we can model and remove multipath errors from our code and carrier observations. SNR data allow us to do just that. After extracting the direct signal (A_d) to reveal the SNR due only to multipath (SNR_MP), this remaining time series depends only on A_m and ψ. As shown in Figure 2 and Equation 7, SNR due to multipath oscillates with a constituent frequency ω, which is the time derivative of ψ, and has an amplitude envelope equivalent to A_m. Therefore, from SNR due to multipath we can estimate multipath relative phase and multipath amplitude as a function of time.

This idea of modeling SNR data to estimate multipath parameters as time-varying quantities was first explored in a multi-antenna differential environment. This concept was extended to undifferenced SNR data so that carrier-phase errors at single-antenna GPS stations could be modeled and removed. In our implementation, we used wavelet analysis to first separate the direct amplitude from the multipath signal, then estimated the frequency content ω(t) of SNR_MP as a function of time. Using as the primary input to an adaptive least-squares algorithm, we then estimated multipath amplitude and relative phase as a function of time. Substituting these A_d, A_m, and ψ estimates into Equation 5 for carrier-phase multipath yielded a multipath-error correction profile.

A simple example from the Salar de Uyuni, a large salt flat in Bolivia, illustrates the process. For PRN8 observed during September 2002 with an antenna about 1.4 meters above the salt surface, the SNR due to multipath has very clear oscillations with a constituent frequency of approximately 0.0021 Hz (470 second period) (see FIGURE 3). Using frequency estimates as an input, the adaptive estimation algorithm estimates direct and multipath signal amplitudes as well as the multipath relative phase, which is approximately linear with time due to the relatively constant frequency estimate. Figure 3 shows that the modeled SNR_MP closely matches the SNR data, and the carrier phase correction profile closely matches the phase errors.

FIGURE 3. SNR modeling example from the Salar de Uyuni data set, PRN8, ascending arc, in seconds since the beginning of the satellite pass. Real data are given in black, while estimated quantities are colored lines; estimation uses SNR due only to multipath, i.e., after the direct signal has been removed, in linear amplitude units. The goal of SNR modeling is to generate a phase-multipath correction profile, shown in the bottom panel as a red line overlaying phase residuals.

SNR-based phase-error estimation techniques show great promise for removing multipath errors from phase data. For the Salar de Uyuni test session, we derived SNR-based carrier-phase corrections for all satellites in view. By applying these corrections, we achieved a reduction in carrier-phase postfit residual root-mean-square error of up to 20 percent for static positioning, and 1–7 dB reduction in spectral power at multipath periods for kinematic positions.

Power Spectral Maps. Sadly, the complex and time-varying nature of multipath error cannot always be removed. In those cases, a better understanding of the multipath environment (the direction of and distance to reflecting objects) may aid the GNSS analyst. With this information, an analyst could discern the effect of multipath on position solutions, or de-weight multipath-corrupted observations, or simply choose one solution strategy (static, real-time kinematic or RTK, long vs. short occupation, etc.) over another to minimize or avoid multipath effects. For example, short duration but high-frequency multipath errors would be unimportant to someone solving for a single position using 24 hours of data, but that same multipath source could wreak havoc in an RTK survey. A method to evaluate the multipath environment at different frequencies and with a sense or orientation is therefore of great value.

As with the phase-error modeling example above, we accomplish multipath characterization via the frequency content of SNR oscillations, but this time backing out the distance, h (see Equation 3). This distance is directly related to the multipath frequency — nearby objects yield low-frequency errors, distant objects lead to high-frequency errors. By relating the distance, h, to angles (θ,γ) describing the direction and orientation of reflecting objects (Figure 1a), we can fully describe the multipath environment.

In this application, dubbed power spectral mapping, a wavelet transform is applied to each satellite’s SNR time series to extract multipath power estimates over a range of frequencies or height values. The 3-D power vs. frequency vs. spatial coordinate data cube is then sliced into frequency bands of interest (i.e., height ranges), and all data contributing to a frequency band are combined. The signal power is assigned to the satellite’s location and projected onto a “sky plot.” This type of plot has four quadrants for north, south, east and west; concentric rings indicate satellite elevation angle; the center of the plot is the zenith while the outer ring is the horizon. This combination and projection process forms a map depicting the multipath characteristics of a GPS site.

These maps can help the analyst determine the source of multipath errors. For example, at first glance the permanent International GNSS Service (IGS) GPS station MKEA (see PHOTO) on Mauna Kea volcano in Hawaii seems to be multipath-free as it is surrounded by nothing but jagged rocky ground — uneven ground (relative to the GNSS wavelength) should create a diffuse multipath signature.

Mauna Kea GPS station MKEA, facing northwest

The SNR data tell a different story, with strong coherent oscillations (see FIGURE 4) over a range of frequencies. By conducting wavelet analysis for all satellites in view, the combined power spectral maps (see FIGURE 5) show very strong reflections coming from the south-southeast and northwest, the location of volcanic cinder cones. Although rocky, these cinder cones generate strong multipath reflections. The sloped hillsides can be broken into a set of discrete reflectors at different distances, creating multipath oscillations at different frequencies over each satellite pass. For a more in-depth discussion of MKEA multipath and other power spectral map examples, see “Mapping the GPS Multipath Environment Using the Signal-to-Noise Ratio (SNR),” listed in Further Reading.

FIGURE 4. Example SNR profile from MKEA (top panel) as a function of time, in linear amplitude units after direct signal contributions have been removed. The bottom panels show wavelet power at different periods (colored lines), which are averaged together to form the wavelet power over 30–60 and 60–90 seconds-period bands of interest (heavy black lines).

FIGURE 5. GPS L1 power spectral maps for MKEA SNR data for four different frequency bands (given as periods in upper right-hand corner of each plot). Figure is reproduced from “Mapping the GPS Multipath Environment Using the Signal-to-Noise Ratio (SNR).”

Soil Moisture. Manuel Martin-Neira is credited with introducing the idea, in 1993, that reflected GPS signals could be used for scientific studies. Since then, GPS reflection studies for ocean altimetry and winds, soil moisture, and snow sensing have all been discussed in the literature. These studies typically use an antenna pointed to optimize Earth reflections and specifically designed to track reflected (LHCP) signals. This means that antennas designed to suppress ground reflections, such as those used by the geophysical, geodetic, and surveying communities, are not used.

Motivated by our studies showing that multipath effects could clearly be seen in geodetic-quality data collected with multipath-suppressing antennas, we proposed that these same GPS data could be used to extract a multipath parameter that would correlate with changes in the reflectance of the ground surface. In our initial study, we used data from an existing IGS GPS site in Tashkent, Uzbekistan, and concentrated on SNR reflectance changes caused by rain and subsequent drying of the soil. While the correlation between the SNR data and precipitation models was strong, we lacked proper ground instrumentation to demonstrate that we were measuring true soil moisture changes.

Subsequently, together with other colleagues, we carried out an experiment designed to more rigorously demonstrate the link between GPS SNR and soil moisture. Specifically, we were interested in using GPS reflection parameters to determine the soil’s volumetric water content — the fraction of the total volume of soil that is occupied by water, an important input to climate and meteorological models. Traditional soil moisture sensors (water content reflectometers) were buried in the ground at multiple depths (2.5 and 7.5 centimeters) at a site just south of the University of Colorado in Boulder. Precipitation data were also collected. Using a fixed frequency, Equation 7 was used to model the SNR data and estimate an amplitude and phase offset on each day. FIGURE 6 shows phase estimates converted to water content for six satellites that pass over the same ground south of the GPS antenna. We specifically concentrated on these six satellites because they transmit the new L2C signal, which yields superior SNR data compared to the L1 C/A-code signal.

FIGURE 6. Variation in volumetric water content (VWC) from multiple GPS satellites (colored dots) and water content reflectometers buried at 2.5-centimeter depth (data range given by grey shaded region). Daily precipitation totals in blue. Figure is reproduced from “Use of GPS Receivers as a Soil Moisture Network for Water Cycle Studies.”

Figure 6 shows excellent agreement between in situ sensors and the GPS multipath parameters. Soil moisture values rise within hours of a precipitation event, and then drop over approximately one week as the soil dries. It is important to note that the GPS SNR data are sensing much larger spatial regions (hundreds of square meters) whereas the soil probes measure values over a very small soil region (100 centimeters square). Climate scientists desire soil moisture measurements that have large footprints, and SNR data from some existing GPS stations are uniquely poised to provide this scale of soil moisture measurements.

Conclusions

Under the simplified multipath model discussed here, SNR data have a defined relationship to both carrier-phase and pseudorange multipath errors. Although SNR is traditionally used only as a measure of signal tracking, we have demonstrated some applications that use this common but underutilized observable to identify potential multipath sources, model and remove phase multipath errors, or retrieve soil moisture content from ground reflections. All of these applications are predicated upon accurate and precise SNR measurements, which conform to the simplified multipath model. Not all receivers are created equal in this respect, thus care must be taken in selecting reliable SNR data for analysis.

Acknowledgments

We acknowledge technical support from UNAVCO and funding from the National Science Foundation. We thank our colleagues Eric Small, John Braun, Ethan Gutmann, Valery Zavorotny, and Penina Axelrad.

Manufacturers

The Salar de Uyuni and Mauna Kea data sets were obtained from Ashtech (now Magellan Professional) Z-12 receivers using Allen Osborne Associates (acquired by ITT Communications Systems) AOAD/M_T element antennas while the soil moisture experiment data set was from a Trimble NetRS receiver fed by a model TRM29659.00 choke ring antenna with SCIT radome.

ANDRIA BILICH is a geodesist with the National Geodetic Survey’s Geosciences Research Division in Boulder, Colorado. Her research interests include GPS multipath characterization, antenna calibration, and precision improvements to high-rate positioning for geoscience applications. She received her B.S. in geophysics in 1999 from the University of Texas and a Ph.D. in aerospace engineering in 2006 from the University of Colorado. Dr. Bilich was the recipient of the 2007 Parkinson Award from The Institute of Navigation for her dissertation titled Improving the Precision and Accuracy of Geodetic GPS: Applications to Multipath and Seismology.

KRISTINE M. LARSON received a B.A. in engineering sciences from Harvard University in 1985 and a Ph.D. in geophysics from the Scripps Institution of Oceanography, University of California at San Diego, in 1990. Since 1990, she has been a faculty member in the Department of Aerospace Engineering Sciences at the University of Colorado at Boulder. The primary focus of her work is developing and improving GPS applications for measuring plate tectonics, episodic slip, volcanic deformation, ice-sheet motion, timing, seismic waves, soil moisture, and snow depth.

Further Reading

• Multipath Basics and Mitigation Techniques

“Introduction to Multipath: Why is Multipath Such a Problem for GNSS?” by A. Bilich in GPS World’s online Tech Talk, posted January 19, 2008.

“GPS Receiver Architectures and Measurements” by M.S. Braasch and A.J. van Dierendonck in Proceedings of the IEEE, Vol. 87, No. 1, January 1999, pp. 48–64.

“Conquering Multipath: The GPS Accuracy Battle” by L.R. Weill in GPS World, Vol. 8, No. 4, April 1997, pp. 59–66.

“Multipath Effects” by M.S. Braasch in Global Positioning System: Theory and Applications, edited by B.W. Parkinson, J.J. Spilker Jr., P. Axelrad, and P. Enge, Vol. 1, Chp. 14, American Institute of Aeronautics and Astronautics, Washington, D.C., 1996.

• Multipath Ray Tracing

“Development and Testing of a New Ray-Tracing Approach to GNSS Carrier-Phase Multipath Modelling” by L. Lau and P.A. Cross in Journal of Geodesy, Vol. 81, No. 11, pp. 713–732, 2007 (d
oi: 10.1007/s00190-007-0139-z).

• Assessing and Modeling Multipath Using Signal-to-Noise Ratios

“Mapping the GPS Multipath Environment Using the Signal-to-Noise Ratio (SNR)” by A. Bilich and K. M. Larson in Radio Science, Vol. 42, RS6003, 2007 (doi:10.1029/2007RS003652).

“Scientific Utility of the Signal-to-Noise Ratio (SNR) Reported by Geodetic GPS Receivers” by A. Bilich, P. Axelrad, and K. M. Larson in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 26–28, 2007, pp 1999-2010.

“Modeling GPS Phase Multipath with SNR: Case Study from Salar de Uyuni, Bolivia” by A. Bilich, K. M. Larson, and P. Axelrad in Journal of Geophysical Research, Vol. 113, B04401, 2008 (doi:10.1029/2007JB005194).

• Using GPS to Estimate Soil Moisture

“Using GPS Receivers to Measure Soil Moisture Fluctuations: Initial Results” by K.M. Larson, E. E. Small, E. Gutmann, A. Bilich, P. Axelrad, and J. Braun in GPS Solutions, Vol. 12, No. 3, pp. 173–177, 2008 (doi: 10.1007/s10291-007-0076-6).

“Use of GPS Receivers as a Soil Moisture Network for Water Cycle Studies by K.M. Larson, E. E. Small, E. D. Gutmann, A. L. Bilich, J. J. Braun, and V. U. Zavorotny in Geophysical Research Letters, Vol. 35, L24405, 2008 (doi:10.1029/2008GL036013).

• Measuring Reflected GPS Signals from Space

“Reflecting on GPS: Sensing Land and Ice from Low Earth Orbit” by S.T. Gleason in GPS World, Vol. 18, No. 10, October 2007, pp. 44–49.

“A Passive Reflectometry and Interferometry System (PARIS): Application to Ocean Altimetry” by M. Martin-Neira in ESA Journal, Vol. 17, No. 4, 1993, pp. 331–355.

October 1, 2009
Innovation: One Year in Orbit
GIOVE-B E1 CBOC Signal Quality Assessment

By Matthias Söllner, Christian Kurzhals, Wolfgang Kogler, Stefan Erker, Steffen Thölert , Michael Meurer, Maktar Malik, and Manuela Rapisarda

GIOVE-B has been in orbit for just over one year. How well is it performing? In particular, what can we say about one of GIOVE-B’s pioneering features: its E1 CBOC signal? In this month’s column, we take a detailed look at a particular monitoring and assessment program set up to examine the GIOVE-B signals and discuss some of its initial CBOC results. The successful operation of this program bodes well for its use in future validation campaigns.

INNOVATION INSIGHTS by Richard Langley

THE SECOND GALILEO TEST SATELLITE, GIOVE-B, was launched on April 27, 2008, and began transmitting navigation signals a few days later. It joined its older sibling, GIOVE-A, which was placed in orbit over two years earlier. Standing for Galileo In-Orbit Validation Element, the GIOVE satellites constitute the first in-orbit test phase in the development of the Galileo navigation system.

In addition to securing the frequencies for the system, the satellites are being used to assess key technologies for the full Galileo constellation. The GIOVE test phase will be followed by the In-Orbit Validation (IOV) phase during which four IOV satellites will be launched, two at a time, aboard Soyuz rockets from Europe’s spaceport in French Guiana. Together with a preliminary ground network, the IOV satellites will be used to validate the Galileo system as a whole, using advanced system simulators. The launches are expected to occur by the end of 2010.

But before the IOV phase can begin, a thorough analysis of the performance of the GIOVE satellites must be carried out to minimize any difficulties with the IOV satellites. This includes monitoring and assessing the different signals broadcast by the satellites.

The GIOVE satellites can transmit on all three Galileo frequencies, E5, E6, and E1 (also known as L1) but only on two simultaneously (either E1-E5 or E1-E6). A variety of modulation types can be transmitted on the different frequencies by both satellites to test their use for the different Galileo services to be implemented for the operational constellation. These include alternative binary offset carrier (BOC) and quadrature phase shift keying on E5 and cosine BOC (BOCc) and binary phase shift keying on E6. On E1, the satellites have different capabilities. Although both satellites can transmit BOCc on this frequency, GIOVE-A can additionally transmit a single BOC signal with a subcarrier frequency of 1.023 MHz and a spreading code chipping rate of 1.023 MHz (BOC(1,1) ) whereas GIOVE-B transmits a more versatile multiplexed composite BOC or CBOC, which linearly combines BOC(1,1) and BOC(6,1). The CBOC signal is being transmitted by GIOVE-B to explore its performance, usability, and any possible side effects including its use in receivers designed to track a BOC(1,1) signal.

GIOVE-B has now been in orbit for just over one year. How well is it performing? In particular, what can we say about one of GIOVE-B’s pioneering features: its E1 CBOC signal? In this month’s column, we take a detailed look at a particular monitoring and assessment program set up to examine the GIOVE-B signals and discuss some of its initial CBOC results. The successful operation of this program bodes well for its use in future validation campaigns.

The first measurements of the navigation signals transmitted by the second Galileo test satellite, GIOVE-B, were recorded during the night of May 7, 2008, following the successful launch from Baikonur a little over a week earlier on April 28. During the In-Orbit Test (IOT) phase of the mission, which lasted about three months, a program of intensive measurements was carried out by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt or DLR) and the Astrium subsidiary of the European Aeronautic Defence and Space Company (EADS) using the 30-meter high-gain antenna at Weilheim/Lichtenau, near Munich, Germany. These and follow-on activities were performed under a bilateral agreement between the European Space Agency (ESA) and DLR’s Institute of Communication and Navigation on the exploitation of the GIOVE satellites.

The first measurements indicated that the navigation payload suffered no damage or degradation with regard to power and signal-in-space (SIS) quality during the launch of the satellite. After the successful completion of the IOT phase, the satellite configuration was maintained for long intervals to allow stability and long-term quality assessments within the ESA GIOVE mission activities and to stabilize ground segment operation. DLR and Astrium continued their measurements and analysis on GIOVE-B signals, focusing on modulated power and on modulation and correlation quality. The observation intervals included an eclipse season, when GIOVE-B spends a fraction of its orbit in the Earth’s shadow. Any impact of the corresponding temperature variations on signal transmission characteristics and key navigation parameters was of special interest.

This article focuses on modulated spectral power analysis of GIOVE-B signals for the detection of frequency and elevation-angle-dependent variations in the transmitted signal spectrum and power. Also, a detailed in-phase/quadrature-phase (I/Q) sample analysis is presented, focusing on modulation correctness and correlation distortions. These assessments are based on sample files of a few seconds of navigation signals as received by a calibrated high-gain antenna and recorded with the BayNavTech Signal Experimentation Facility (BaySEF). We have evaluated, in particular, correlation loss and S-curve bias since these parameters are very sensitive to onboard signal distortions although their reliable evaluation is quite challenging. In this article, we concentrate on the analyses of the E1 composite binary offset carrier (CBOC) modulation.

We discuss the GIOVE-B measurement and evaluation parameter results from the initial IOT phase and from later phases, including those from an eclipse period. This comparison of measurement results — spread over the first year of operations — demonstrates the excellent stability of signal power, modulation, and correlation quality of the GIOVE-B signals.

GIOVE-B E1 Signal
GIOVE-B can transmit navigation signals either simultaneously in the Galileo E5 and E1 bands or in the E6 and E1 bands. At E1, with a center frequency of 1575.42 MHz, GIOVE-B transmits three signal components called E1-A, E1-B, and E1-C. The E1-A signal has a BOCcos(15,2.5) modulation, with 2.5575 MHz code chip-rate and a binary cosine-type subcarrier modulation of 15.345 MHz. The B- and C-components have CBOC(1,6,1,10/1) modulation. Within this type of multiplexed BOC implementation, the code chips are provided at a constant rate of 1.023 MHz, modulated with a composite quaternary subcarrier with rates of 1.023 and 6.138 MHz. The latter part, called the BOC(6,1) subcarrier, has a relative power of 1/11 and is added to the BOC(1,1) binary subcarrier in CBOC-B and subtracted for CBOC-C. From the beginning of May 2008 until July 2009, GIOVE-B transmitted signals 96.8% of the time. Considering the experimental nature of the satellite, this represents a very successful operation.

Signal Quality and Relevance
In GNSS operations, signal quality assessment generally refers to the behavior of the navigation signals as transmitted from individual satellites. In this article, we assess two major aspects: the transmitted signal power and the frequency-transfer distortions of the satellite relative to the ideal signal definitions.
Why should we consider these aspects in particular? For transmitted signal power, the answer is obvious. In addition to proof of compliance with regulatory declarations, verification and monitoring of signal power is relevant for the system provider to commit to certain navigation service performance.

Concerning frequency-transfer distortions, the answer may be less obvious. The relevance is obtained via the impact of distortions on a receiver’s correlation function. First, the available maximum correlation power for a receiver binary replica may be affected. This is due to changes in the power sharing of the signal components within the complete signal and due to reduced matching of distorted transmitted signals with the ideal receiver replica. Second, the shape of the correlation function may suffer from asymmetric distortions, leading to receiver-dependent biases in the discriminator lock point. The most relevant receiver parameters are the input bandwidth and the discriminator type, especially the discriminator spacing (early-late spacing). With respect to this, user receivers and receivers in the control system ground segment (used to derive the satellite orbit and clock parameters) may differ. This leads not only to timing but also to positioning errors, if the distortions are different for different satellites.

These are the main reasons why we need to analyze and control the corresponding distortions for the Galileo system.

For an accurate assessment of transmitted signal power and frequency distortions, we had to obtain measurements with a highly directive antenna. This is essential in order to lift the instantaneous signal far above the noise floor and to avoid environmental distortions from interference as well as multipath.

But why don’t we consider also other aspects of signal quality such as the correctness of the navigation message or the stability of hardware delays and clocks? Because measurements with the highly directive antenna are not so appropriate for an assessment of these parameters. Instead, continuous monitoring over weeks or longer is preferred, based on measurements from a network of distributed navigation receivers. Such monitoring likely will be discussed in other publications.

Figure 1: Illustration of S-curve bias parameter.

Evaluation Parameters
For the assessment of the satellite’s radiated power in the individual navigation bands, absolute calibrated spectral measurements are evaluated for spectral flux densities integrated over frequency. These parameters provide a first insight into spectral asymmetries and variations over time.

Other parameters are used to evaluate the distortions in the instantaneous transfer characteristics of the satellite. They are derived from wide-band recorded baseband-signal samples of up to a few seconds duration.

Initially, we consider the I/Q probability density of the signal after Doppler frequency shift removal, well known from communication system analysis as scatter plots. Secondly, as introduced in the previous section, we want to quantify the impact of transfer distortions on the navigation performance obtained for ideal navigation receivers. The corresponding acquisition and tracking performance is based on the correlation function.

We define the normalized correlation function with respect to ideal receiver properties in order to separate the satellite transmit distortions of interest from receiver distortions according to the following equation:

with
- the preprocessed baseband signal, SBB-PreProc, with down-converted nominal center frequency, full Doppler removal, and brick-wall-filtered to a bandwidth of interest;
- the reference-signal, SRef, providing the ideal binary (or, for CBOC, quaternary) baseband receiver replica signal;
- the integration period, Tp, often corresponding to the primary code period of the reference signal under consideration.
From this normalized correlation function, we can derive the primary relevant navigation parameters, which are, as previously mentioned:
- correlation loss
- early-late spacing dependency of the code-loop-discriminator lock point and S-curve bias.
Figure 2: GIOVE-B E1 CBOC measured and ideal normalized power spectral density.

Correlation Loss. For a given (distorted) signal, the correlation loss (CL) quantifies the loss in correlator output power relative to an ideal signal. This can be formulated by

where

It should be noted that the ideal baseband signal here is the multiplexed signal including all signal components, also band limited with a brick-wall filter to the bandwidth of interest.

S-Curve Bias. The navigation receiver obtains the (noiseless) code delay by following the zero crossing of the code discriminator. The output as a function of delay resembles the letter “S” or its reflection and is called the S-curve. For asymmetric distortions in the correlation function, it turns out that different code-tracking loops may have different lock points, as illustrated in FIGURE 1 for an arbitrary example.

To quantify this effect with a reasonable compromise between parameter complexity and practical value, a non-coherent (early minus late) power discriminator is considered over a wide range of early-late spacings, . This refers to the code discriminator of

with its lock point, , defined by

.

Then, the spreading of the lock point is the S-curve bias, SCB, given by

,

considering all δ in the range [0, δ_max], with

Those interested in additional candidate signal-quality parameters, more detailed descriptions, or a theoretical analysis of the impact of satellite distortions on navigation performance parameters should consult the paper “GNSS Offline Signal Quality Assessment” listed in Further Reading.

Figure 3: GIOVE-B E1 CBOC received power flux on May 11, 2008

Weilheim Measurement Setup

As previously mentioned, for accurate measurements of the various signal-quality parameters, the signal needs to be lifted above the noise and multipath, and any interference needs to be suppressed considerably. Moreover, as the signal quality from the satellite needs to be assessed separately, any measurement-system transfer distortions need to be calibrated out as much as possible.

That’s why DLR installed a measurement and calibration system at their 30-meter high-gain dish antenna at Weilheim for GIOVE signal performance measurements. This measurement setup was completed for high capacity signal recording with one rack of the BaySEF equipment in cooperation with EADS Astrium.

The 30-meter antenna, as shown in the opening graphic, is the main core of this verification facility. This antenna is based on a shaped Cassegrain system with elevation over azimuth mount, with higher than 50-dB gain and a beam width around 0.5° at L-band. The absolute position accuracy of this antenna is 0.001° in each direction. The signals are directed from the parabolic main reflector to the measurement cabin via a hyperbolic sub-reflector, a waveguide, and a second flat sub-reflector. One big benefit of this construction is the direct access to the installed feed in the cabin and the possibility to place the complete measurement equipment next to the feed, avoiding long connection cables.

The signals are recorded with BaySEF and for individual frequency bands (selected by switchable band pass filters) and also with a vector signal analyzer (VSA) of at least 80 MHz bandwidth. Moreover, a signal with up to 300 MHz bandwidth can be recorded with a digital oscilloscope, if the VSA is used for down conversion. Interfaces to other measurement equipment, such as navigation receivers, have already been prepared for future extension. The whole setup is referenced to a highly stable cesium frequency standard. We essentially use the BaySEF equipment as a high-capacity multiband bit grabber in this setup.

Figure 4: GIOVE-B E1 CBOC received power frequency asymmetry on May 11, 2008

The main BaySEF applications are the verification and monitoring of GNSS SIS and support for the design of applications based on parametric software-receiver evaluations. The key features most relevant for the signal-quality assessment discussion of this article are:
- Simultaneous processing (down conversion, etc.) and synchronized recording of up to four frequency bands;
- 3-dB RF bandwidth of more than 100 MHz at E5 and more than 50 MHz in other bands (E5a, E5b, L2, E6, E1);
- Maximum recording capacity of 120 megabytes per second per frequency band;
- Flexible decimation and quantization;
- Recording capacity of 0.6 terabytes per frequency band, corresponding to more than 80 minutes at the maximum recording rate;
- Remote control of data acquisition from, for example, the EADS Astrium premises in Ottobrunn.
Figure 5: GIOVE-B E1 CBOC received power flux on July 3, 2009

Figure 6: GIOVE-B E1 CBOC received power frequency asymmetry on July 3, 2009.

Figure 7: Scatter plot of GIOVE-B L1-CBOC (black crosses indicate ideal constellation).

Measurement Calibration

Accurate system calibration is the key to reliable signal quality measurements. To achieve a combined absolute measurement uncertainty significantly less than 1.0 dB (required for EIRP assessments), it is essential to calibrate precisely every used part of the system. In addition to all RF components of the receiving system, this also includes the high-gain antenna itself.

For the characterization of the high-gain antenna, two values are assessed: antenna pointing accuracy and gain. A pointing offset of about 0.04° was measured exactly with a known L-band pilot signal from the Artemis satellite and corrected in the antenna control. For gain characterization over the complete L-band frequency range of interest, the radio “star” Cassiopeia A is used. Cas A (actually a supernova remnant) is one of the strongest wide-band radio emitters in the northern hemisphere. With the help of the well-known flux density of Cas A, the gain-to-noise-temperature ratio (G/T) can be measured. After precise determination of the system noise temperature, T, the antenna gain, G, itself can be found.

For online calibration of absolute gain drifts in the measurement system, a frequency-and-power-stabilized signal generator is used in combination with two power meters.

The relative frequency transfer distortions of the receiving system are calibrated with two techniques. A network analyzer periodically provides precise measurements of gain and phase of the RF path from the antenna feed to the measurement devices.

To include also down-converting measurement devices such as the BaySEF, the injection of wide-band calibration signals is used, and simultaneously measured with a commercial digitizer (VSA).

The desired in-band measurement system transfer characteristic (relative to the VSA-characteristic, which is assumed to be ideal) is then extracted by means of de-convolving the calibration signal sampled by the device to be calibrated with the VSA-sampled reference signal.

Corresponding transfer characteristics obtained for the RF path (from network analysis) and for the BaySEF (from wide-band calibration signals) were combined to derive the equalization filter applied in post-processing the recorded samples.

Power Measurement Results

For analysis of the transmitted signal power of the GIOVE-B E1 CBOC and its variation, we have recorded a large number of spectral measurements over single satellite passes. A typical example of a single power spectral density measurement — here normalized to unit power — is shown in FIGURE 2, overlaid on the ideal spectral envelope. After absolute power calibration, we integrate the spectral power flux density over a reference bandwidth of 40.92 MHz and over the individual main lobes of the BOC(1,1) and BOCc(15,2.5) components, as illustrated in Figure 2. This procedure is used to detect the variation of transmitted signal power and possible signal asymmetries over time.

Parameter results are shown first for an early satellite pass during the IOT campaign on May 11, 2008 in FIGURE 3. Main variations in this figure are as typically expected from the cut of the measurement pass through the satellite antenna pattern. Also, the overall main-lobe power of the BOC(1,1) and the BOCc(15,2.5) components are similar, but with a strong asymmetry between upper and lower main-lobe power of the BOCc(15,2.5) component.

A closer look at the time-dependency of this asymmetry is given in FIGURE 4, showing a stable low power difference of about 0.2 dB between the BOC(1,1) main lobes and a mean power difference of 0.8 dB between the BOCc(15,2.5) main lobe with ±0.2 dB variations over time.

The second record was captured more than a year later, with similar results as shown in FIGURE 5, which indicate a stable transmission power. Only the mean main-lobe asymmetry of the BOCc(15,2.5) signal, as shown in FIGURE 6, is slightly smaller and with a different shape in its time-dependency. The measurement passes surely provided different cuts through the satellite antenna pattern. Therefore, we assume that these variations mainly indicate some antenna-pattern frequency dependency, as will be emphasized also in the following signal quality measurement results. For precise characterization, measurements and evaluations for many more satellite passes would be required.

Figure 8: GIOVE-B E1 correlation function shape of CBOC components

Figure 9: GIOVE-B E1 correlation function shape of BOCc(15,2.5) component

Figure 10: GIOVE-B E1 example of correlation-loss results for COBC-B

Signal Quality Results

In this section, we will show some GIOVE-B E1 signal quality results as obtained from BaySEF measurement data collected at Weilheim. The results presented are from two passes with the satellite in E1 CBOC-transmission mode:
- May 9, 2008, a few days after the signals were switched on
- September 29, 2008, a pass when the satellite was in eclipse for one hour.
The modulation type of the E1 signal can be seen from the scatter plot, shown in Figure 7, which is derived from about 50 millisecond-signal-samples after Doppler removal. The overlay, with the eight phase points of the ideal constant envelope signal, allows clear identification of the interplexed CBOC signal. Due to bandwidth limitation, distortions, and noise jitter, the individual phase states have been enlarged, and transition traces become visible. Also, the symmetry of phase states becomes slightly deformed. How much this affects measurable navigation performance is not directly obvious from such a plot.

A better indicator of navigation performance is the E1 interplex CBOC correlation function shapes as shown in FIGURE 8 (CBOC-B and -C) and FIGURE  9 (BOCc(15,2.5)), respectively. As for all the following evaluations, the E1 signals were brick-wall band-limited to 40.92 MHz (40 × 1.023 MHz), which was the performance bandwidth of interest, and up-sampled to a rate of 575 MHz. The differences in shapes are not due to distortions but due to different signs of the BOC(6,1) subcarrier in the B and C channels. Note the imaginary part due to signal distortions has been amplified 10 times to stress its presence.

For the shape of the BOCc(15,2.5) correlation function, a small asymmetry in the real part is visible when compared to the ideal band-limited autocorrelation function. This signal distortion effect might be relevant, leading to a higher chance for false lock in acquisition and tracking. However, current receivers have no problems in tracking these signals.

A direct visual assessment of these shapes does not allow us to draw many conclusions. More quantitative evaluations provide the performance parameters of correlation-loss and lock-point-bias behavior.

Example results of the correlation-power evaluation for the GIOVE-B CBOC-B signal component are shown in FIGURE 10, with the red curve showing the correlation power of succeeding code periods relative to the total signal (which also includes the CBOC-C and BOCc(15,2.5) signal components). A curve of similar shape, shown in blue, is obtained for the ideal reconstructed signal using the specified codes and actual data bits of all components synchronized to the input signal. Obviously, the strong jitter of more than 0.1 dB is due to code cross-correlations. The correlation loss is obtained by taking the difference (see Figure 10b), which has a much lower variation with a maximum value of about 0.02 dB. This variation is still dominated by residual distortion differences of the code-cross-correlation values of the GIOVE-B codes as can be seen when accounting for the fact that equally colored dots in the figure correspond to equal code cross-correlations. Despite the variation, the negative loss is remarkable. This corresponds to a gain in usable signal power relative to the ideal case and is due to a stronger bandwidth limitation. This analysis indicates that the signal power of the wide-band signal component, BOCc(15,2.5), is more strongly reduced than for the narrow-band CBOC component, providing effectively a gain in relative correlation power for CBOC.

Next, we consider the lock-point bias of a noncoherent power discriminator as a function of early-late spacing (over the relevant range) for the CBOC-C signal component in FIGURE 11, evaluated for about 60 succeeding code periods. Again, different colors indicate different code-cross-correlation values. The corresponding S-curve bias is obtained by evaluating the peak-to-peak variation of this lock-point bias and is shown in FIGURE 12. Over this short period of 0.5 seconds, a quite stable value of about 1325 picoseconds is obtained with only 25 picosecond standard deviation mainly due to residual code-cross-correlation effects and residual noise.

Figure 11: GIOVE-B E1 example of lock-point bias dependency on early-late spacing for CBOC-C

It should be noted that all evaluation results include not only the satellite distortions but, in general, measurement distortion contributions. In fact, accurate measurement system calibration is one of the most critical issues for exact signal quality assessment of satellite transfer characteristics.

Mutual evidence of successful calibration is gained from the fact that similar results for correlation loss and S-curve bias of the CBOC-signal components have been obtained from measurements carried out by ESA together with Surrey Satellite Technology and the Science and Technology Facilities Council at the Chilbolton Observatory near Andover, England. However, even for the same measurement periods and perfect calibration, identical results may not be expected, as will be discussed here.

Figure 12: GIOVE-B E1 example of S-curve-bias evaluation result for CBOC-C

In addition to the signal-quality assessment of individual snapshot measurements, variation over observation direction and over time is of major relevance. Therefore, these evaluations have been performed for many snapshots of two single passes almost five months apart. The passes and measurement points mapped to the directions as seen from a satellite-antenna-fixed coordinate system are shown in Figure 13.

In FIGURE 14, the results of correlation loss and S-curve bias are shown for both passes mapped to the satellite antenna off-axis angle. For a monotonic x-axis, a sign was added to this angle here, with negative values for the ascending part of the pass and positive values for the descending part as seen from Weilheim.

Figure 13: GIOVE-B measurement points of BaySEF transformed into satellite antenna coordinates (off-axis angle, antenna-azimuth angle)

Figure 14: GIOVE-B E1 correlation-loss variation results over satellite passes.

The correlation-loss results for both passes vary about ±0.1 dB around 0.7 dB for BOCc(15,2.5), around -0.5 dB for CBOC-C and around -0.55 dB for CBOC-B. Also, the measured S-curve-bias results are in the same range for both passes, which is about ±100-200 picoseconds around 1200 picoseconds for CBOC-B and CBOC-C and ±2 picoseconds around 23 picoseconds for BOCc(15,2.5). Furthermore, these plots show smooth variations over the antenna off-axis angle, obviously also with some azimuth dependency due to the different shapes for each pass. It is remarkable that the relative shapes of the correlation-loss and S-curve-bias plots are so similar.

Even if measurement system instability may be a contributing factor, most of the variations are thought to be due to the directional dependency of the satellite antenna pattern. See also the similarity of results from both passes at the one end of the high off-axis angles, corresponding to similar azimuth angles. These results indicate that for full characterization of signal quality over the satellite antenna pattern, further well-calibrated measurements over several passes would be required.

During the measurement pass of September 29, 2008, a few measurement points were taken when the satellite was in eclipse. Corresponding results marked by black points in the figures show almost no impact of the eclipse on correlation loss and S-curve bias. Further measurements and evaluations would be required to confirm finally that the larger changes afterwards are not due to the eclipse.

Conclusions
This article described some of the GIOVE-B navigation SIS performance characterizations carried out by DLR and EADS Astrium in collaboration with ESA during the past year. These characterizations were achieved using a very accurate measurement system, calibrated in absolute power, relative amplitude, and phase over several frequency bands. For this purpose, several calibration approaches have been adopted and are still being optimized.

The similarity of results from measurements spaced several months apart indicates excellent long-term stability of the considered characteristics. Also, it has been demonstrated that a satellite-eclipse period seems not to affect modulation and correlation quality in a relevant manner.

As expected and already known from previous work, the satellite provides some frequency-dependent directional variations in its transmitted signal power characteristics, which also affects signal quality.

During the described campaign, the observed variations can be considered as moderate. A continuation of the monitoring activity is under consideration, to further improve the coverage of the characterizations.

During these GIOVE-B measurements and evaluations, all involved teams gained considerable experience in accurate characterization of the navigation signals transmitted from the satellite, and the operation of instruments and signal evaluation was thoroughly verified and cross-validated. The knowledge and experience gained will be very useful for future navigation satellite validation campaigns.

Acknowledgments
We would like to thank DLR’s German Space Operations Center for use of the Weilheim antenna and the colleagues who operate and maintain it. BaySEF is part of the BayNavTech program, which is supported by the Bavarian Government (Ministry for Economic Affairs, Infrastructure, Transport and Technology). The activity reported in this article was performed under a bilateral agreement between ESA and DLR’s Institute of Communication and Navigation on the exploitation of GIOVE satellites and the GIOVE signal-quality-characterization effort has been supported by ESA. This article is based on the paper “One Year in Orbit – GIOVE-B Signal Quality Assessment from Launch to Now” presented at the European Navigation Conference GNSS 2009, held in Naples, Italy, May 3–6, 2009.

Manufacturers
Measurement equipment included a Rohde & Schwarz GmbH & Co. KG (www.rohde-schwarz.com) FSQ26 vector signal analyzer. The EADS Astrium facility did not use commercial receivers to capture the GIOVE-B signals discussed in this article.

MATTHIAS SÖLLNER is senior expert on navigation signal engineering. CHRISTIAN KURZHALS and WOLFGANG KOGLER are navigation signal engineers focusing on payload aspects and performance verification, respectively. All are working at the Astrium GmbH subsidiary of the European Aeronautic Defence and Space Company N.V. (EADS).

STEFAN ERKER and STEFFEN THÖLERT work on GNSS validation and signal analysis at the German Aerospace Centre in Oberpfaffenhof-en with MICHAEL MEURER, who is responsible for performance issues concerning the Galileo system.

MAKTAR MALIK is the payload system manager for GIOVE-B at the European Space Research and Technology Centre in Noordwijk, The Netherlands, where he works with MANUELA RAPISARDA, who is a radio navigation engineer providing system support to the Galileo Project Office.

Further Reading: Click here for references related to this article.
September 1, 2009
Innovation: Where Is GIOVE-A Exactly?

Using Microwaves and Laser Ranging for Precise Orbit Determination

By Erik Schönemann, Tim A. Springer, Michiel Otten, and Matthias Becker

Though Galileo’s GIOVE-A is a test satellite not necessarily ready for scientific use, orbit analyses with a reduced accuracy can help to identify weaknesses and suggest improvements. This month, the authors share work being carried out to precisely determine the orbit of GIOVE-A using SLR and microwave observations. This preliminary investigation will benefit the procedures to be implemented for the future Galileo constellation.

INNOVATION INSIGHTS by Richard Langley

WE USE THEM FOR LISTENING TO MUSIC, for routine surgeries, for making a point in a presentation, and even for hanging pictures straight. Of course, I’m talking about lasers. Invented in 1960, the laser (an acronym for light amplification by the stimulated emission of radiation) has become ubiquitous in modern society. Every CD and DVD player has one. Many printers use them. But lasers are also used in a wide range of industrial and scientific applications including determining the orbits of satellites through satellite laser ranging (SLR).

In the SLR technique, pulses of laser light from a ground reference station are directed at satellites equipped with an array of corner-cube retroreflectors, which direct the pulses back towards a collocated receiving telescope. By accurately measuring the two-way travel times of the pulses and knowing the location of the station and other operating parameters, the positions of the satellites can be determined. A network of SLR reference stations around the globe is used to monitor the orbits of satellites over time and their variations have been used by scientists to improve our knowledge of the Earth’s gravity field; to study the long term dynamics of the solid Earth, oceans, and atmosphere; and even to verify predictions of the General Theory of Relativity.

The first SLR measurements were obtained from the Beacon Explorer-B satellite, which was launched in October 1964. Since then, dozens of satellites equipped with corner-cube retroreflectors have been launched including a number of radio-navigation satellites. Every GLONASS satellite is equipped with retroreflectors and two GPS satellites have been equipped—SVN35/PRN05 and SVN36/PRN06. The COMPASS-M1 satellite in medium Earth orbit carries retroreflectors, as do both GIOVE-A and –B, the Galileo test satellites.

Precise orbit determination of radio-navigation satellites using SLR has the advantage of being unaffected by any onboard satellite electronics and associated signal biases. Radiometric observations of a satellite’s microwave signals, on the other hand, are influenced by the satellite’s clock, for example, and its effect must be estimated to obtain precise (and accurate) satellite orbits for navigation and positioning. Therefore, a comparison of SLR- and microwave-derived orbits can be very useful for studying the performance of the data measurement and orbit-determination processes of both techniques.

In this month’s column, we take a look at some work being carried out to precisely determine the orbit of the GIOVE-A test satellite using SLR and microwave observations. This preliminary investigation will benefit the procedures to be implemented for the future Galileo constellation.

“Innovation” is a regular column that features discussions about recent advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering at the University of New Brunswick, who welcomes your comments and topic i deas. To contact him, see the “Contributing Editors” section on page 6.

The navigation office of the European Space Operations Centre (ESOC) is engaged in various activities using observations of the Galileo test satellite, GIOVE-A (Galileo In-Orbit Validation Element-A), recorded at the Galileo Experimental Sensor Stations (GESS). The work includes the assessment of the quality and performance of GIOVE satellite observables and the testing and improvement of orbit-determination software. These activities support the long-term goal of advancing the scientific applications of the future Galileo constellation.

Since the launch of GIOVE-A on December 28, 2005, various tests have been carried out to analyze the quality of the new code (pseudorange) and carrier-phase observables derived from tracking the satellite’s microwave signals. All of these tests demonstrate the advantages of the new signal structure compared to that of legacy GPS signals. In general, the reduction of the noise by factor of 4-5 as well as a reduction of the code multipath by approximately a factor of 1.2 (GPS C1C versus GIOVE-A C1B/C1C) could be seen.

As the comparison of observations is done indirectly (GPS and GIOVE-A have different orbits) and the databases used for most analyses published up to now is sparse, a deeper analysis of the signal quality parameters seems appropriate, especially as data quality has a direct impact on the precision of orbit determination. Our analyses, presented in the first half of this article, are based on a broad base of data from most of the stations in the GESS network. Because of the difficulty in accessing the phase multipath directly, we first evaluated the signal strength and the code multipath, which gave the first hint of the multipath behavior. In order to compare GPS and GIOVE-A data directly, only data received from the same elevation angles and azimuths were used. Subsequently, we present an analysis of the phase residuals derived by precise point positioning.

The second part of this article focuses on the precise orbit determination or POD of the GIOVE-A spacecraft. The Navigation Package for Earth Observation Satellites (NAPEOS) software used at the ESOC Navigation Support Office allows microwave (radiometric) and satellite laser ranging (SLR) observations to be used either separately or together. The two methods are different due to different tracking networks and the different sensitivity of the observables to atmospheric effects and in their noise levels. We will present the orbit results focusing on internal orbit consistency checks and SLR validation of the microwave-based orbits.

Data Analysis

We first describe the procedures used for analyzing the microwave data followed by those used for the SLR data.

Microwave Analysis. For the GIOVE-A signal analysis and precise orbit determination we used the RINEX data from all of the GESS stations available from the GIOVE archiving facility (see TABLE 1). All stations are equipped with GPS/Galileo antennas, built by Space Engineering S.p.A. and Galileo Experimental Test Receivers (GETRs), built by Septentrio. The data, containing tracking data of all GPS satellites and the GIOVE-A satellite, is given in the RINEX 3.00 data format with a sampling interval of 1 second. To save on storage space for the long-term analyses, such as orbit determination, the RINEX data is decimated to 30-second samples and Hatanaka-compressed, using a test version of the Hatanaka software for the RINEX 3.00 format.

The signal analyses shown here were carried out using GNU Octave, an open-source program for performing numerical computations similar to Matlab, and different scripts developed by the Institut für Physikalische Geodäsie at the Technische Universität Darmstadt. These analyses cover a selection of the designated Galileo signals recorded by the GESS within the time span from December 16 to 27, 2006. Within this time period, the current GPS signals, as well as the GIOVE-A signals E1 and E5, shown in TABLE 2, were recorded. The table also shows the signal components as well as the RINEX observation-type identifiers, which we use in this article.

The stations used for the analyses show a quite similar level of performance in general. There are stations with different behaviors for single signals, as for example GIEN with a stronger code multipath behavior on C1B and C1A, but no station with a considerably different performance level could be identified. The averaging over the data from all sites reduces the station-dependent effects such as multipath and the atmosphere to a large extent, and gives a good indication of the mean signal performance.

The analyzed phase residuals were taken from the processing carried out for the second part of this article. Hence, they include observation data over an extended period of 149 days and were limited to the GIOVE-A C1C/L1C and C7Q/L7Q signals.

This extended data period is from December 12, 2006 (day of year 346), until May 26, 2007 (day of year 146). During this interval, there is a period where no GIOVE-A data was available due to maintenance of the spacecraft. This gap occurred from February 12 to 28, 2007. So in total we have analyzed 149 days of microwave data. Because there are some differences between the results before and after this gap in February, many of the statistics are given for the first and second part separately. The first part covers December 12, 2006, until February 11, 2007; the second part covers March 1, 2007, until May 26, 2007.

We performed the precise orbit determination using the NAPEOS software, a general-purpose software package for orbit determination, prediction, and control, supporting all phases of an Earth-observation mission in terms of mission preparation and operations.

For the GIOVE-A analysis, the three main NAPEOS programs we used are GnssObs, Bahn, and Multiarc. GnssObs reads, cleans, and decimates the RINEX data and converts the data into the NAPEOS internal tracking-data format. The NAPEOS tracking-data format contains the ionosphere-free linear combination, for both code and phase, of the RINEX observations. For GPS, the ionosphere-free linear combination is based on the combination of C1P and C2P code and L1P and L2P phase measurements. GIOVE-A offers several different observables allowing for many different ionosphere-free observations. For most of the work presented in this article, we have used the ionosphere-free linear combination of the C1C and C7Q and L1C and L7Q observations for code and phase respectively.

The next module, Bahn, performs the parameter estimation. In this step, we use the ionosphere-free code and phase observations at a sampling interval of 5 minutes, and we have applied an elevation angle cut-off of 5 degrees. The data is processed in batches of 24 hours, thus resulting in 1-day-arc solutions. The estimated parameters in these daily solutions are the GIOVE-A state vector (position and velocity), five dynamical orbit parameters from the extended Center for Orbit Determination in Europe (CODE) orbit model, a GIOVE-A clock offset for each epoch, all receiver clock offsets for each epoch, one GPS-GIOVE-A “intersystem bias” parameter per day for each station except for a selected reference station, and the carrier-phase ambiguities (integers not resolved). The station coordinates are estimated but tightly constrained (1 millimeter) to their a priori value. We obtained the a priori station coordinates by combining the full set of daily solutions.

Despite the fact that the 13 GESS stations provide very good global coverage, it is expected that 24-hour solutions will not give the most precise GIOVE-A orbit estimates. To generate longer arc solutions, we have used the Multiarc program. This is a tool that has recently been added to the NAPEOS software package. It allows for a rigorous combination of normal equations, also referred to as normal equation stacking, which are generated by Bahn. During the normal equation combination, the satellite orbit parameters may also be rigorously combined, thus effectively leading to multi-day orbital arcs. For the work presented in this article, we have used Multiarc to generate solutions with arc lengths of 1, 2, 3, 4, and 5 days. We also used Multiarc to compute accurate a priori station coordinates by stacking all available 1-day normal equations.

Satellite Laser Ranging

Besides the 13 GESS stations, GIOVE-A is also tracked by more than 17 different SLR stations around the world. For most periods of the mission, the tracking has been consistent enough to allow for GIOVE-A POD using only the SLR data. As the SLR data is completely independent of the microwave data, the resulting orbit solutions will be to a large extent independent as well and thus can be used to give an indication of the achieved precision of the different microwave solutions.

The orbit determination strategy used for the SLR solutions is very similar to the one used for the microwave orbits with the main difference being the increased arc-length of 7 days. The same satellite parameters are estimated as with the microwave solutions: the GIOVE-A state vector and five dynamical orbit parameters from the extended CODE orbit model. No further parameters need to be estimated and all corrections applied to the SLR data are according to the International Earth Rotation and Reference Systems Service 2003 standards and, for station coordinates, we used those from the rescaled International Terrestrial Reference Frame 2005 solution. As the noise level of the SLR data is very low, the measurements can also be directly used to give an indication of the precision of the radial position components of the different microwave solutions by computing the SLR residuals without using them in the estimation process itself.

Combined Microwave and SLR Analysis. In this step, the SLR data was added to the microwave data in the 24-hour solutions. For the data weighting, we used 100 millimeters for SLR and 1000 millimeters and 10 millimeters for GIOVE-A and GPS code and phase observables respectively. The only change in the analysis strategy in this case was that we now processed the SLR data in 24-hour solutions and not in 7-day batches. All the processing options remained as described in the two previous sections. The resulting 1-day solutions, or rather the associated normal equations, were used in Multiarc to generate combined solutions of different arc lengths.

Microwave Data Quality

We now take a detailed look at the quality of the microwave data in terms of signal-to-noise ratio (SNR), code-tracking noise and multipath, carrier-phase-tracking noise, and carrier-phase residuals.

Signal-to-Noise Ratio. The SNR (or equivalently carrier-to-noise-density ratio, C/N₀) is strongly dependent on the satellite transmitter, the signal path through the atmosphere, and the receiver configuration (ground station, antenna, receiver, cable, etc.). Hence the SNR cannot be seen as an absolute value. The SNR is specific to the position, the equipment, and the time. Furthermore, the determination of the SNR values depends on the receiver and the firmware used. As a result, SNR values from different receivers cannot be readily compared. Nevertheless, using only one type of receiver, assuming similar effects on all the different signals at the same epoch, and taking averages over a long time span, we expect the relationships among the signals to be constant. Based on this assumption, we can use the SNR values given in the GESS RINEX files without adjustment.

To compare the GPS with the GIOVE-A SNR values, we ordered the corresponding SNR values of all stations on all days by satellite position into a grid with widths of 5 degrees in azimuth and 5 degrees in elevation angle. For the evaluation, we took the grid cells occupied by both GPS and GIOVE-A values and computed the median over all the cells of equal elevation angle. The median per elevation-angle bin for each signal is shown in FIGURE 1.

FIGURE 1. Signal-to-noise ratio, GPS versus GIOVE-A

As can be seen from the figure, the signal strength of the GIOVE-A C8Q observable ranks best, followed by the GPS C1C, GIOVE-A C7Q, C5I/C5Q, C1A, and C1B/C1C. The weakest signal is found for the GPS C1P/C2P observable, with a maximum signal strength of 40 (receiver-dependent unit, approximately dB-Hz) at the zenith. Comparing the GPS open signals versus GIOVE-A, GPS C1C is considerably stronger than the GIOVE C1B/C1C. According to the GPS and Galileo interface control documents, GIOVE-A C1B/C1A should show up with a stronger signal strength than GPS C1C. The power levels guaranteed on the Earth’s surface are -160 dBW for GPS and -158 dBW for the future Galileo satellite signals except for the BOC(10,5) and BOC(n,m) modeled signals, for which a power level of even -155dBW is guaranteed. But looking at the SNR values shown in Figure 1, we see that the GIOVE-A C1B/C1C is worse by approximately 4 dB than the GPS C1C. But keeping in mind that GIOVE-A is an experimental satellite, an increase of the signal power for the future operational Galileo satellites should improve the signal performance above that shown in this article.

Code-Tracking Noise. For signals containing data and pilot components, as in the case of those from GIOVE-A, the code-tracking noise can easily be computed as the difference between the data and the pilot signal. The advantage of this computation scheme is that both signals are influenced by identical error sources (atmospheric errors, multipath errors, receiver errors, etc.). Based on the assumption of equal uncertainties in the two components, we divided the resulting noise values by the square root of two to specify the noise level of each part according to the laws of error propagation. TABLE 3 shows the code-tracking noise for the two analyzed GIOVE-A codes sorted by elevation angle. The median code-tracking noise is 0.62 meters for C1B/C1C and 0.35 meters for C5I/C5Q, for observations below an elevation angle of 5 degrees. For the C1B and C1C code measurements, the noise median stays below 0.2 meters for an elevation angle above 25 degrees, whereas the median for the C5I and C5Q code measurements for elevation angles above 35 degrees even comes down below 0.1 meters. The results discussed above are consistent with the code-tracking noise values published previously.

Code Multipath. We computed the relative code multipath effects as code minus phase differences assuming the amplitude of phase multipath to be insignificant compared to the amplitude of the code multipath. Ionospheric effects were taken into account by using the phase measurements on two frequencies in the usual way:

In this equation, CMP_x is the estimate of the multipath error on the code, P_x and L_x are the code and phase measurements of the same frequency, while L_y is the phase measurement used to correct the frequency-dependent ionospheric effect. The constant, , describes the relationship of the ionospheric behavior for the two frequencies.

In order to compare the code multipath level of GPS versus GIOVE-A, we sorted the multipath values using a grid covering the sky with widths of 5 degrees for both elevation angle and azimuth as before. FIGURE 2 shows the median standard deviation of the code multipath values, derived in each grid cell per day and station, versus the elevation angle. No significant difference between GPS C1C and GIOVE-A C1B and C1C, the open code signals on G1/E1, could be found. The code multipath behavior of the GPS precise codes are comparable with the GIOVE-A C5I, C5Q, and C7Q, whereas the C8Q shows the least code multipath effects closely followed by the GIOVE-A C1A, the public regulated service signal.

FIGURE 2. Code multipath, GPS versus GIOVE-A

Carrier-Phase-Tracking Noise Analyses. In the same manner as that carried out with the code, we computed the GIOVE-A carrier-phase-tracking noise as the difference of the two components (pilot minus data). To accommodate the effect of error propagation, the resulting errors were divided by the square root of two. The resulting phase-tracking noise values were sorted by elevation angle and can be found in TABLE 4.

In conformity with the theory that the phase-tracking noise is independent of the modulation scheme, both signals (L1B/L1C and L5I/L5Q) show the same results in units of cycles. Looking at the results in units of distance, GIOVE-A L1B/L1C shows up with a mean phase noise of 0.7 millimeters and L5I/L5Q with 0.9 millimeters. These values confirm those of previous studies.

Carrier-Phase Residuals. Phase residuals contain the phase tracking noise, multipath, as well as all unmodeled remaining errors such as antenna calibration inaccuracy and tropospheric effects. The magnitude of the residuals can be seen as an indicator for the observation and model accuracy as well as for measurement quality.

The following analyses are based on the ionosphere-free linear combination (GPS L1C/L2P, GIOVE-A L1C/L7Q), computed with NAPEOS. The analyses include data of the 13 GESS over a period of 149 days.

To compare the GPS and GIOVE-A residuals, we sorted them into a grid with a width of one degree in both satellite azimuth and elevation angle. Only data in overlapping grid locations were compared to make sure the data was affected in a similar way by multipath or other disturbances.

To properly interpret the results, we should mention that for GIOVE-A, 0.06 percent of the ambiguities (2501) were not fixed correctly whereas for GPS all ambiguities were fixed correctly. Looking at the GIOVE-A observations that were correctly fixed, we find a significantly larger number of rejected observations. The number of rejected observations is less by one third for GPS (6 percent) as for the GIOVE-A (9 percent) data.

Due to the small number of GIOVE-A observations for elevation angles above 86 degrees, the outlier-cleaned mean as well as the standard deviation at this elevation-angle range are not meaningful. For all elevation angles, GIOVE-A residuals show a lower standard deviation than GPS, indicating a superior performance of GIOVE-A signals.

Phase and Code Validation in Processing. Looking at the quality of the code and phase measurements on the different signals, it is conspicuous that GIOVE-A C1A/L1A and C8Q/L8Q rank best, whereas for the current processing of GIOVE-A data, usually the C1C and C7Q signals are used. This leads to the question of which is the best signal combination for GIOVE-A. Hence, we processed 10 days of GIOVE-A data, using different signal combinations. Presently the processing of the C8Q/L8Q signals is not yet implemented in NAPEOS. However, we were able to process the GIOVE-A C1A/L1A – C7Q/L7Q combination. The root-mean-square (RMS) of the code results were reduced by a factor of approximately 1.4 using L1A/C1A compared to L1C/C1C, whereas the RMS of the phase observations showed only a minor improvement. Furthermore, there is a higher number of rejected observations with L1A/C1A. Further analyses have to be carried out to evaluate the potential benefits of the different signal combinations.

Orbit Quality

In this section, we assess the quality of our precise orbit determination solutions. We have three sets of different orbit solutions. Set 1 is made up of the 7-day solutions based solely on SLR observations. Set 2 consists of the solutions based on the microwave observations using 1- to 5-day arcs. Set 3 consists of the solutions based on a joint analysis of the microwave and SLR observations also using 1- to 5-day arcs.

First, we assess the orbit quality by looking at the internal consistency of the solutions. For the two sets using microwave observations, the internal orbit consistency is done using an orbit fit. This will not tell us much about the absolute quality of the solutions but it will indicate the optimal arc length and whether adding the SLR observations to the microwave data improves the orbit estimates.

Secondly, we validate the orbits by determining the SLR residuals. Of course, the solutions that used SLR observations should perform better than the microwave-only solutions. However, the validation of the microwave orbits against the SLR observations will give us a good impression of the absolute accuracy of our orbits.

As a third test, we compare the best orbit (best arc length) of each of the three sets (set 1 only has one arc length) against each other. This should give us another indication of the quality of the orbits.

Internal Orbit Consistency. To determine the internal orbit consistency of the different solutions we make an orbit fit. For this orbit fit test, we used the middle 24 hours of two consecutive solutions and fit one 48-hour arc through these two parts. The satellite orbit was modeled by estimating the satellite state vector and all nine parameters of the extended CODE orbit model. The RMS of this fit gives us an indication of the internal consistency of the orbit estimates. For longer arcs, the RMS of fit should go down because the solutions are not fully independent of each other. So a lower RMS for the longer arc solutions is expected. On the other hand, this means that if the RMS does not go down with increasing arc length that we have reached the limit of our modeling capabilities. Furthermore, comparing the internal orbit consistencies of equal length solutions will tell us which solution has a better internal consistency. The results of this internal orbit consistency check are given in TABLE 5. The table gives the mean of the 2-day RMS over all processed days. The mean is given separately for the first and second part of the observation interval (see above) and also for the total observation interval.

Table 5 shows several interesting results. First of all, it shows that the results of part 2 of the observation interval are significantly better than the results from part 1. The reason for this is unclear since the statistics from the 1-day solutions, such as the residual RMS and number of observations, did not change significantly after the observation gap. The improvement, however, is very significant. The second observation is that the results including the SLR data are significantly better compared to those using only the microwave data. This is true for all arc lengths! As expected, we see a significant improvement of the internal consistency when going from 1-day arcs to 3-day arcs. The 4-day arcs show only a slight improvement compared to the 3-day arcs. The 5-day arcs do not show a significant improvement. This indicates that with the current observations and modeling techniques, the optimal arc length for precise orbit determination seems to be around 3 to 4 days.

SLR Validation. In this section, we look at the SLR residuals obtained from the different orbit solutions. We generated a clean SLR dataset by using the SLR-only orbit to remove any outliers in the SLR observations. The total number of valid SLR normal points for the entire period is 3520 observations from 17 different SLR stations. (A normal point is an average of a number of individual laser returns.) The number of observations for the first part of the observation period is 796 points from 12 stations and for the second part, there were 2724 normal points from 17 stations. For two of the three solutions, the SLR data has been used in the orbit determination process so the residuals will give a too-optimistic indication of the orbit quality.

As can be seen from TABLE 6, the 3-day solution based on the microwave-only data has the lowest SLR residuals and indicates a radial precision of around 100 millimeters. A similar behavior can be seen in the microwave plus SLR solution with the exception of the 1-day solution (and to a smaller extent also the 2-day solution) where the orbit solution is mainly driven by the SLR data, but the quality as can be seen from the internal consistency of the orbit is poor. Interestingly, there is a large improvement in SLR residuals for the microwave plus SLR solution, although the number of SLR data points is only 2 percent of the total tracking data in the combined solution. The values for the SLR-only solution are included in the table to give an indication of the lowest possible SLR residuals one could expect by combining the microwave and SLR data.

Orbit Comparison. To get an indication of the overall orbit quality, the best solutions were compared against each other for the second period of observation. TABLE 7 gives the RMS differences between the SLR only (SLR), 3-day microwave only (micro), and the 3-day microwave and SLR solution (micro+SLR) for the radial, along-track, and cross-track position components as well as the norm (3D).

As expected, the largest difference is between the SLR-only and microwave-only solutions giving a total (norm) orbit difference of 652 millimeters. As a major part of the SLR tracking from GIOVE-A comes from European stations, the quality of the SLR solutions is directly correlated with the ability of the European stations to track GIOVE-A. Bad weather over Europe can lead to data gaps for more than 24 hours, affecting the orbit quality. It is interesting to see the large impact the SLR data has on the combined solution. As mentioned before, the SLR data is only around 2 percent of the total tracking data but has a significant impact on the orbit solution as can be seen from the difference between the microwave-only and microwave-plus-SLR solution.

Based on the analysis presented above, we conclude that the 3-day solution using both microwave and SLR observations has provided the best orbit estimates.

Conclusion

The analyses of the observation data quality (signal quality) confirmed the good results from prior analyses for code multipath behavior and code noise. GPS C1C and the GIOVE-A C1B/C1C show a comparable multipath behavior, whereas the GPS precise codes C1P/C2P are comparable to the GIOVE-A C5I, C5Q, and C7Q. The least code multipath behavior could be found for GIOVE-A C8Q observable, closely followed by the GIOVE-A C1A. Based on this, the combination C1A/L1A – C8Q/L8Q should show the best noise behavior within the data processing scheme.

The results given in this article demonstrate that the 13-station GESS network allows us to determine the orbit of the GIOVE-A satellite quite accurately (~200 millimeters) using only microwave observations. The SLR validation of the microwave orbits gives an RMS of 100 millimeters (one-way range RMS). This result gives an absolute value for the orbital error. Of course, the SLR observations mainly tell us something about the radial orbit errors; the along- and cross-track errors could be much higher. To obtain accurate GIOVE-A orbit estimates, we need to keep the orbits and clocks of the GPS satellites, tracked simultaneously with the GIOVE-A satellite, fixed using the International GNSS Service (IGS) final orbit and clock products. Furthermore, an arc length of 3 days should be used. The microwave-based orbit estimates may be improved by adding the available SLR observations into the orbit-estimation process. Although there are relatively few SLR observations, they do have a significant positive effect on the orbit estimates, improving the internal consistency from 52 to 41 millimeters. Also, the validation of the orbits using the SLR observations shows a significant improvement. However, this is not an independent validation because the same SLR observations were used in the orbit determination.

The results presented in this article, even though based on observations from the GIOVE-A test satellite, can be considered as a first attempt towards establishing an optimal data processing approach for the future Galileo satellite constellation.

Acknowledgments

This article is based on the paper “GIOVE-A Precise Orbit Determination from Microwave and Satellite Laser Ranging Data – First Perspectives for the Galileo Constellation and Its Scientific Use” presented at the 1st Colloquium on the Scientific and Fundamental Aspects of the Galileo Program, held in Toulouse, France, October 1-7, 2007.

ERIK SCHÖNEMANN studied geodesy at the Technische Universität Darmstadt (TUD), Germany, writing his diploma thesis at the University of New South Wales, Sydney, Australia. Since receiving his diploma from TUD in April 2005, he has been working for the Institute of Physical Geodesy at TUD on GNSS station calibration and validation and analyses of GIOVE-A and GIOVE-B data.

TIM SPRINGER received his Ph.D. in physics from the Astronomical Institute of the University of Berne (AIUB) in 1999. He has been a key person in the development of the Center for Orbit Determination in Europe, one of the IGS analysis centers, located at AIUB. Since 2004, he has been working for the Navigation Support Office (NSO) at the European Space Operations Centre (ESOC) of the European Space Agency (ESA) in Darmstadt. In this group, he has led the development of the new ESOC GNSS software, which is used for most GNSS activities at NSO including GIOVE-A and -B analyses.

MICHIEL OTTEN obtained a degree in aerospace engineering from Delft University of Technology in 2001. He has been working for ESOC’s NSO since 2002. His main role within NSO is the precise orbit determination of low Earth-orbiting satellites equipped for SLR, DORIS, and GPS tracking. He is also responsible for ESA’s International DORIS Service Analysis Centre activities.

MATTHIAS BECKER is a full professor of geodesy and director of the Institute of Physical Geodesy, TUD. He received his diploma and Ph.D. in geodesy from TUD in 1979 and 1984, respectively. He is responsible for research and teaching in the fields of physical geodesy and satellite geodesy.

FURTHER READING

• GIOVE-A
“Meet GIOVE-A: Galileo’s First Test Satellite” by E. Rooney, M. Unwin, A. Bradford, P. Davies, G. Gatti, V. Alpe, G. Mandorlo, and M. Malik in GPS World, Vol. 18, No. 5, May 2007, pp. 36–42.

“Galileo Signal Experimentation” by M. Hollreiser, M. Crisci, J.-M. Sleewaegen, J. Giraud, A. Simsky, D. Mertens, T. Burger, and M. Falcone in GPS World, Vol. 18, No. 5, May 2007, pp. 44-50.

• GIOVE Tracking Network
“GIOVE Mission Sensor Station Receiver Performance Characterization: Preliminary Results” by M. Crisci, M. Hollreiser, M. Falcone, M. Spelat, J. Giraud, and S. La Barbera in Proceedings of Navitec 2006, the 3rd ESA Workshop on Satellite Navigation User Equipment Technologies, Noordwijk, The Netherlands, December 11-13, 2006.

• GIOVE Tracking Performance
“Performance Assessment of Galileo Ranging Signals Transmitted by GSTB-V2 Satellites” by A. Simsky, J.-M. Sleewaegen, M. Hollreiser, and M. Crisci in Proceedings of ION GNSS 2006, the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 26-29, 2006, pp. 1547–1559.

“Code and Carrier Phase Tracking Performance of a Future Galileo RTK Receiver” by T. Pany, M. Irsigler, B. Eissfeller, and J. Winkel in Proceedings of ENC-GNSS 2002, the European Navigation Conference, Copenhagen, Denmark, May 27-30, 2002.

• Multipath Mitigation in Modernized GNSS
“Comparison of Multipath Mitigation Techniques with Consideration of Future Signal Structures” by M. Irsigler and B. Eissfeller in Proceedings of ION GPS/GNSS 2003, the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 9-12, 2003, pp. 2584–2592.

• GIOVE Orbit Determination
“Estimation and Prediction of the GIOVE Clocks” by I. Hidalgo, R. Píriz, A. Mozo, G. Tobias, P. Tavella, I. Sesia, G. Cerretto, P. Waller, F. González, and J. Hahn in Proceedings of the 40th Annual Precise Time and Time Interval (PTTI) Meeting, Reston, Virginia, December 1-4, 2008.

• Satellite Laser Ranging
“GIOVE’s Track: Satellite Laser-Ranging Campaigns” by M. Falcone, D. Navarro-Reyes, J. Hahn, M. Otten, R. Piriz, and M. Pearlman in GPS World, Vol. 17, No. 11, November 2006, pp. 34–37.

“The International Laser Ranging Service: Current Status and Future Developments” by W. Gurtner, R. Noomen, and M.R. Pearlman in Advances in Space Research, Vol. 36, No. 3, 2005, pp. 327–332 (doi:10.1016/j.asr.2004.12.012).

“Laser Ranging to GPS Satellites with Centimeter Accuracy” by J.J. Degnan and E.C. Pavlis in GPS World, Vol. 5, No. 9, September 1994, pp. 62–7.

July 1, 2009
Innovation: GPS L5 First Light

A Preliminary Analysis of SVN49’s Demonstration Signal

By Michael Meurer, Stefan Erker, Steffen Thölert, Oliver Montenbruck, André Hauschild, and Richard B. Langley

Great excitement surrounds the activation of a new transmitter from a satellite — an occasion dubbed first light. Research groups around the globe joined the GPS Wing in monitoring and analyzing the first L5 signals from space. We describe the equipment and procedures used to capture and analyze SVN49’s signals and give an assessment of their characteristics.

INNOVATION INSIGHTS by Richard Langley

ON APRIL 10, a new type of radio signal was transmitted from space. I am referring, of course, to the L5 demonstration signal from the Block IIR-M satellite SVN49, launched on March 24. The L5 signal, the second of two new civil GPS signals, will be standard on the next generation of GPS satellites — the Block IIFs — and its frequency band was duly registered with the International Telecommunication Union (ITU) back in 2002. But satellite operators only have seven years after filing a frequency application to start transmitting signals from the designated orbit, and delays in launching the first Block IIF satellite meant that GPS could lose the allocation. The GPS Wing and its contractors determined that the best way to secure the L5 frequency was to add an L5 demonstration payload to one of the remaining modernized Block IIR satellites. And so SVN49 made history with the inaugural broadcast of L5 with just a few months to spare before the clock ran out on the ITU filing.

Great excitement always surrounds the first photons captured by a new telescope or other detectors of electromagnetic signals. Or when a transmitter is activated for the first time. Just as we do for the dawning of a new day, we call this occasion first light. Research groups around the globe joined the GPS Wing in monitoring and analyzing the first L5 signals from space, including a group of scientists and engineers from Germany and Canada. This month the group describes the equipment and procedures used to capture and analyze SVN49’s signals and gives an assessment of their characteristics.

“Innovation” features discussions about advances in GPS technology and applications as well as fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. To contact him, see “Contributing Editors.”

A key feature of GPS modernization is the addition of the L5 civil signal to the suite of signals transmitted by the satellites. The introduction of such a signal on a different carrier frequency than that used by the legacy L1 GPS signal was proposed in the 1995 reports by the U.S. National Research Council and the National Academy of Public Administration on the future of GPS. The reports argued that an unencrypted signal on a second frequency would offer civil users the benefit of ionospheric delay correction, wide-lane carrier-phase ambiguity resolution, improved interference rejection, and faster accuracy recovery in multipath environments.

Studies showed that it would be possible to add a civil signal on the L2 frequency without compromising the military signal. High-precision (and accuracy) civil users had been using the L2 frequency — initially designated for military use only — ever since the first GPS satellites were launched, and through clever (though suboptimum) tracking techniques even after the L2 signals were encrypted. An unencrypted signal on L2 would bring these users a more robust signal as well as affording all civil users the benefits of a second frequency. But unlike the L1 signal, the L2 signal is situated in a part of the radio spectrum not officially protected from interference by other users of the spectrum. So such a second civil signal could not be used for safety-of-life applications such as navigating aircraft.

So, in Vice President Al Gore’s statement of March 30, 1998, on the enhancement of GPS for civil users, the decision to deploy two new civil signals was announced: the civil signal on L2, now known as L2C, and a signal on a new frequency, which became known as L5. Some readers might wonder why this new signal was not designated L3 or L4. Those designations had already been assigned to signals associated with other payloads on the GPS satellites.

Although the Gore announcement proposed to introduce both of the new civil signals with the launch of the Block IIF satellites, the addition of the L2C signal to the legacy signals was deemed a relatively straightforward task and the decision was made to modify the last eight Block IIR satellites for the provision of L2C. The first modernized Block IIR satellite was launched on September 26, 2005, and seven of these satellites are now in orbit.

The frequency selected for the L5 signal, 1176.45 MHz, is in a protected aeronautical radionavigation services (ARNS) band. This frequency, as with frequencies used by all satellite operators, had to be coordinated with the International Telecommunication Union-Radiocommunication Sector (ITU-R). The ITU-R registers frequencies essentially on a first-come, first-served basis, but a user must actually transmit signals on the assigned frequency from the designated satellite orbit type within seven years from the date of filing with ITU-R. This meant that L5 signals had to be transmitted before August 26, 2009, to avoid the potential claim of the frequency by a different country. A decision was made to modify an existing Block IIR-M satellite to carry an L5 demonstration payload. The L5 demo payload, which was developed by Lockheed Martin and its subcontractors, was added to space vehicle number (SVN) 49. SVN49 was launched on March 24, 2009, the seventh modernized Block IIR satellite to be placed in orbit. Also known as PRN1, from the primary pseudorandom noise (PRN) codes assigned to the satellite, the satellite began L5 transmissions on April 10, at 11:58 UTC, and so satisfied the ITU-R filing requirement with a few months to spare.

The L5 Signal Structure

The structure of the future full L5 signal will differ significantly from the legacy L1 signal or even the modernized L2C signal. It is fully described in the Navstar GPS L5 interface document, IS-GPS-705. We present just a brief overview of the signal here.

Two-Component Signal. The full L5 signal will offer two signal components: one with and one without a superimposed navigation data message. The two signal components — in-phase (I) and quadrature (Q) — have equal power. Both will have a minimum received power of –157 dBW. Each component is modulated with a different, but synchronized, L5 PRN code. The in-phase component (the I- or data channel) is further modulated with a 100-symbol per second (sps) symbol stream carrying the navigation message data, and the quadrature component (the Q- or data-free channel, also called the pilot channel) is modulated only with a PRN code. Different, nearly orthogonal PRN codes (referred to as I5 and Q5) are used in the two components to prevent tracking biases by making each component completely independent of the other, except for the underlying carrier phase.

Another novel aspect of the L5 signal design is the use of Neuman-Hoffman (NH) synchronization codes.

Code Structure. As previously mentioned, the I5 and Q5 channels are modulated with different PRN codes. These codes differ significantly from the C/A-, P-, and L2C-codes used on L1 and L2 both in length and chipping rate.

The natural code chipping-rate frequency of 10.23 MHz as provided by the SV atomic frequency standards satisfies a number of requirements for a modernized signal within the bandwidth constraints — increased bandwidth efficiency, improved signal accuracy, immunity to waveform distortion, and improved rejection of narrowband interference. The bandwidth constraints include rejection of out-of-band interference. Accordingly, a 10.23 megachip per second (Mcps) chipping rate, 10 times that of the C/A- and L2C-codes, was adopted for the L5 PRN codes.

Improved Cross-Correlation. There is a trade off between code period and the capability to do direct acquisition. A longer code period provides better cross-correlation properties, but takes longer to search. However, one can speed up an acquisition to some extent with lower code cross-correlation levels.

The L1 C/A-code period is 1023 chips, or 1 millisecond. The desire to maintain that epoch rate of 1 kHz with the 10.23 Mcps chipping rate results in a code period of 10,230 chips. For both the I5 and Q5 ranging codes, the 1-millisecond sequences are the modulo-2 sum of two sub-sequences referred to as XA and XB with lengths of 8,190 and 8,191 chips, respectively. The same XA sequence is used for both I5 and IQ, whereas the XB sequence for I5 is different from that for Q5. The XB sequences are selectively advanced to produce different 1-millisecond-long code sequences. In this way, a large number of unique codes can be generated. Thirty-seven primary code pairs have been designated, of which 32 are reserved for use by GPS satellites (PRNs 1–32). An additional 173 pairs have been defined (PRNs 38–210). PRN sequences 38 through 63 are reserved for satellites.

Demo Signal Verification

The L5 signal transmitted by SVN49 contains only the dataless quadrature component modulated with the PRN63 Q5 sequence. Furthermore, the transmitted L5 signal power and the satellite antenna radiation pattern are different from those expected for the L5 signals to be transmitted by the Block IIF satellites as described in the L5 interface specification.

Over the past few weeks, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt or DLR) has monitored SVN49 using its GNSS verification and analysis facility. The core element of the facility is a 30-meter dish antenna at Weilheim, near Munich, Germany, and is shown in FIGURE 1. The antenna, which is based on a shaped Cassegrain system, has a 30-meter-diameter parabolic reflector and a hyperbolic sub-reflector with a diameter of 4 meters. The L-Band gain of this high-gain antenna is around 50 dB, with a beam width of less than 0.5°. The position accuracy in both azimuth and elevation directions is 0.001°. The antenna’s maximum slewing speeds are 1.5° per second in azimuth and 1.0° per second in elevation angle, allowing it to easily track MEO satellites.

FIGURE 1. GNSS verification and analysis facility with 30-meter high-gain antenna at Weilheim, Germany.

In September 2005, DLR’s Institute of Communications and Navigation established an independent monitoring station for the analysis of GNSS signals using this powerful instrument. For the new challenge, the antenna was adapted to the requirements in the navigation field. A newly developed broadband circularly polarized feed and a new receiving chain including an online calibration system were installed at the antenna during preparations for the GIOVE-B in-orbit test campaign in the spring of 2008.

During this time, intensive work on the system calibration was performed using well-known signals from radio “stars” and EGNOS satellites for the antenna gain determination, and sophisticated calibration methods for the receiving system. The calibration provides an absolute measurement uncertainty significantly less than 1 dB.

Due to the distance of the antenna location from the institute at Oberpfaffenhofen (around 40 kilometers), it was necessary to perform all measurement and calibration procedures during the measurement campaigns under remote control. A software tool was developed that can control any component of the setup remotely. In addition, this tool is able to perform a completely autonomous operation of the whole system by a pre-definable sequence over any period of time. Additional details about the GNSS verification and analysis facility and the calibration techniques used can be found in the literature cited in Further Reading.

A detailed signal-in-space (SIS) analysis of the new L5 signal transmitted by SVN49 was conducted by recording several passes with the GNSS verification and analysis facility. A high elevation-angle transit of SVN49 every night allows a long observation time for each satellite pass. To ensure precise tracking of the satellite with the high-gain antenna, we used the latest two-line element sets from the U.S. Air Force Space Command.

The first signals transmitted by the satellite on the L5 frequency were captured during the pass on April 10. Compared to later measurements, the power of the L5 payload signal was measured with a lower output level on this first pass. This points to a power “fade in,” which is a common procedure in commissioning a new satellite payload. A controlled and slow heating of the payload elements avoids possible damage caused by the out-gassing of the power amplifiers, for example.

The SIS analyses that we performed using the high-gain antenna will be described for one example satellite pass recorded on April 29. During this pass, the satellite reached an elevation angle of around 80° and was visible for about seven hours (see FIGURE 2). A set of spectral snapshots as well as time sample records for the L1, L2, and L5 signals were processed and adjusted with the corresponding calibration values during a post-processing stage.

FIGURE 2. Skyplot of SVN49 pass at Weilheim, Germany, on April 29, 2009.

Time and Frequency. A first view of the captured spectrum snapshot in FIGURE 3 shows the L5 signal and its typical binary phase-shift-keyed (BPSK) spectral shape. The signal is significantly band limited by the used front-end filters of the satellite’s L5 payload. This ensures the required spectral separation from the adjacent L2 signal of the satellite, as the L5 signal must not interfere with the operational L2 frequency. Overlaying the theoretical spectral mask of the L5 BPSK signal, we note a slight asymmetry of the spectral shape. The two side lobes differ around 2.5 dB in their peak power level (see Figure 3). Spectral asymmetries of that kind typically result from frequency selectivity in the RF transmitter chains in satellite payloads, including the amplifiers and antennas.

FIGURE  3. L5 spectrum plot from data recorded on April 29.

FIGURE 4 shows a temporal snapshot of the L5 signal after wiping off the Doppler frequency shift due to satellite orbital motion. Figure 4 (left) depicts a snapshot of 10 microseconds for the I and Q channels. It can be seen, that in compliance with the requirements of the L5 signal explained in the introduction of this article, the signal is a bi-level signal with a chipping rate of 10.23 Mcps. Plotting the normalized histogram of the L5 signal, one obtains the normalized I/Q probability density function (PDF) diagram of the L5 signal shown in Figure 4 (right). The constellation diagram shows a remaining deformation of the Q component after Doppler removal. Although the L5 signal transmitted by the test payload only contains the dataless Q5 component, a non-negligible contribution can be seen in the I channel. This slight distortion may stem from a nonlinear and frequency-dependent amplification of the Q baseband signal leading to crosstalk between the Q and I channels.

FIGURE  4. (left) L5 I and Q time samples; (right) L5 I/Q probability density function (PDF).

Signal Code Sequence. With the use of the high-gain antenna, it is possible to look in detail at the transmitted L5 code chips. The signal is raised high above the noise floor and, after Doppler wipe off, allows us to compare the received code sequence with the theoretical code sequence for the PRN63 Q channel. FIGURE 5 shows an example for the first 10 microseconds of the code — both for the measured L5 signal and the expected theoretical code. The analysis performed also for several full code periods shows that the demo payload’s Q5 code structure is in full compliance with the “theoretical” code described in the official signal interface document.

FIGURE  5. Comparison of measured and theoretical code sequences.

Power of Received Signals. The GNSS verification and analysis facility is fully calibrated, allowing highly accurate absolute measurements of GNSS signal power levels. We have used the system to evaluate the SVN49 signal power levels as received on the ground. FIGURE 6 shows the different signals transmitted in the L1, L2, and L5 frequency bands in terms of the received power per square meter versus elevation angle of the SV during its pass. It can be seen that there is a significant elevation-angle dependency of the L5 received power (about 18 dB between low and high elevation angles) compared to L1 and L2 (with a variation of about 3 dB). In this measurement, the combined power of the I and Q channels is plotted for the signals. So this means that the L1 and L2 signal measurements include the power of the C/A-, P(Y)-, and M-codes. Such a strong elevation-angle dependency is not typical of signals radiated by GPS satellites. However, the L5 signal is radiated using the legacy L1/L2 Block IIR-M satellite antenna, which is to the authors’ knowledge not optimized for the L5 frequency.

FIGURE  6. Absolute received power for SVN49 L1, L2, and L5 signals on April 29, 2009.

In the spectrogram plot of FIGURE 7, which was generated by plotting all recorded L5 spectra versus elevation angle, the impact of this elevation-angle dependency of the received power can be detailed for the complete frequency range. The side lobes of the BPSK signal are only clearly visible in the spectrogram at higher elevation angles.

FIGURE  7. Spectrogram for L5 signal received on April 29, 2009.

Signal Tracking

In parallel with the detailed signal validation using the high-gain antenna and vector signal analyzer, an effort has also been made to track the new GPS L5 signal using conventional correlating GNSS receivers. Given the relevance of L5/E5 signals for future aeronautical applications and the ongoing transmission of such signals from the GIOVE satellites, a growing number of commercial receiver manufacturers have announced receivers supporting this frequency band. However, due to the special nature of the SVN49 test signal (pilot only, with different PRN code designations on L1 and L5) some modifications to receiver software are required to properly track the first GPS L5 signal. In particular, the use of different PRN code designations employed for L1/L2 (PRN1) and L5 (PRN63) is clearly non-standard and requires suitably adapted receiver software, which was provided by the makers of the two receiver types we selected for our test campaign.

Receiver type N is a highly configurable test receiver for L1 and L5/E5a signals developed as part of the Galileo program. It offers a total of 16 tracking channels, which are implemented in a field-programmable gate array and can thus be flexibly adapted for tracking of civil GPS, satellite-based augmentation systems, and the GIOVE-A and -B signals in their respective frequency bands. Receiver type J, in contrast, represents the latest generation of geodetic grade multi-constellation receivers. It uses an advanced application-specific integrated circuit with 216 tracking channels supporting all types of non-military navigation signals in the L1/E1, L2, and L5/E5a bands. Both receivers have been used for some time prior to the launch of SVN49 to track GPS and GIOVE satellites from stations at the University of New Brunswick (UNB) in Canada and at DLR in Germany.

The first measurements of GPS L5 were successfully collected on April 10 with a type N receiver at UNB. While these measurements confirmed the capability to properly track SVN49 in the L5 band, they already revealed a distinct aspect of the GPS L5 test signal that potential users must be aware of. The signal is much weaker at low elevation angles than the L1 signal. Normal carrier-to-noise-density ratios (C/N0) are only achieved at elevation angles of about 60° and higher. On the other hand, the measured C/N0 near zenith may even outperform that of L1 and L2 tracking with sufficient L5 antenna gain. For illustration, FIGURE 8 compares the measured C/N0 values of GPS and GIOVE-A/B signals as obtained with receiver type J and a geodetic antenna at DLR, Oberpfaffenhofen.

FIGURE  8. Comparison of the relative signals strength (expressed as carrier-to-noise-density ratio, C/N0) for GPS (left) and GIOVE-A/B signals (right). The signals are described by their respective RINEX 3.00 data format identifiers, which reflect the type of measurement (S=signal strength), the frequency band (1=L1/E1, 2=L2, 5=L5/E5a) and the signal attribute (C=C/A or L2C, W=P(Y) semicodeless, X=pilot and data).

While not officially confirmed so far, the abnormal variation of the L5 signal strength can best be attributed to a non-standard gain pattern of the satellite transmitter antenna. Apparently, the existing Block IIR-M satellite antenna “farm” has been used to transmit the L5 signal, which results in more directivity than that of the L1 and L2 signals. This results in a weaker signal for receivers further away from the antenna boresight axis, or, equivalently, stations observing the satellite at low elevation angles. Even though the achieved C/N0 of the GPS L5 test signal is lower than that of the direct L1 C/A-code and L2 L2C-code tracking for most of a tracking arc, the signal quality still exceeds that of the semicodeless P(Y)-code tracking on L1 and L2. This makes the signal a valuable basis for experimentation in aviation applications or triple-frequency processing.

To assess the quality of the raw GPS measurements, we made use of the so-called multipath combination of pseudorange and carrier-phase measurements:

The combination is essentially the difference between the pseudorange (P _C5) and carrier-phase measurement (Φ_L5) on the L5 frequency, and therefore measures the sum of the pseudorange multipath (M) and noise (ε). Due to the opposite sign of ionospheric path delays on code and phase measurements, an ionospheric correction is used in the multipath combination, which requires phase measurements on a second frequency (in this case L1). The individual carrier-phase biases are, furthermore, aggregated into a common bias (b). Other than in a traditional zero-baseline test, the multipath combination neither requires a second receiver nor a second satellite transmitting the same signal in space. It is therefore best suited for studying the tracking performance of the new GPS L5 test signal.

Results for receiver types N and J obtained at DLR, Oberpfaffenhofen, are shown in FIGURE 9 for a sample, high-elevation angle tracking pass. Despite obvious differences that can be related to the specific multipath environment and code-smoothing strategies for the two receivers, a high quality is obtained in both cases. For the central three-hour interval, during which the L5 signal was received with normal signal strength, the achieved tracking accuracy clearly outperforms that of the L1 C/A-code signal for the given receivers. For further comparison, FIGURE 10 shows sample results of GIOVE-B E5a tracking with receiver type J. Again, the GPS L5 signal at medium- to high-elevation angles is fully competitive and a notable degradation is only evident when the signal strength is well below the values to be expected in the future operational system.

FIGURE  9. Pseudorange multipath and receiver noise of SVN49 (PRN G01) L5 tracking for a selected pass over Oberpfaffenhofen, Germany, on April 29-30, 2009. Top: receiver type J with geodetic antenna. Bottom: receiver type N with a Galileo antenna. The satellite exceeded an elevation angle of 50° between 20:30 and 23:30 with a peak elevation angle of 80° near 22:00.

FIGURE 10. Pseudorange multipath and receiver noise of GIOVE-B L5 tracking for a high pass over Oberpfaffenhofen, Germany, on April 17, 2009, using receiver type J.

Legacy Signal Anomaly. While the GPS L5 signal transmission by SVN49 is clearly designated as experimental, the legacy signals (that is, the C/A- and P(Y)-code on L1 as well as L2C- and P(Y)-code on L2) were expected to achieve the same level of performance as observed on other satellites of the existing constellation. This is not the case, however, in the L1 band where both the C/A-code measurements and the semicodeless P(Y)-code pseudoranges exhibit a systematic, elevation-angle-dependent bias. This bias is not specific to any of our test receivers and can be similarly observed in heritage receivers employed at the stations of the International GNSS Service (IGS). As an example, FIGURE 11 illustrates the variation of the C/A-code error for high-elevation angle passes of SVN49 over western Canada and Germany. The bias varies between approximately -0.5 meters near the horizon and 1meter near zenith.

The cause of the bias is unclear but resides apparently in the design of the transmitter antenna or signal generation chain. It is exclusively seen on SVN49 and not on other GPS (or GIOVE) satellites, which excludes a possible problem of the receiver antenna or environment. Furthermore, data collected at UNB using the UNBJ IGS station a few days after launch clearly demonstrate that the elevation-angle-dependent L1 bias existed well before L5 signal activation and therefore might not be related to the signal generator. It is unclear to what extent the L1 signal bias can be corrected on the spacecraft and how it will affect the declaration of SVN49 as a fully healthy satellite.

FIGURE 11. Pseudorange errors of SVN49 L1 C/A code tracking for high-elevation-angle passes using a type A receiver at IGS station DRAO in Penticton, Canada (top), and a type J receiver at Oberpfaffenhofen (bottom). The satellite achieved peak elevation angles of about 70° and 80°, respectively, at the two sites.

Conclusions

Tracking and analysis of SVN49’s L5 signal using both the 30-meter dish and code-correlating receivers reveals that it possesses improved signal characteristics with respect to the legacy signals, in particular with regard to its bandwidth, and therefore will allow even more accurate and reliable positioning when the signal is deployed on the future Block IIF constellation.

Acknowledgments

We thank NovAtel and JAVAD GNSS for supplying special firmware, Sébastien Carcanague at UNB, and DLR colleagues at Weilheim for their help. The L5 signal description comes from the Innovation article by A.J. Van Dierendonck and C. Hegarty, September 2000 issue of GPS World.

Manufacturers

Receiver N is the NovAtel (www.novatel.com) EuroPak-15a. Receiver J is the JAVAD GNSS (www.javad.com) Triumph Delta-G2T. Receiver A is an Allen Osborne Associates (AOA) Benchmark ACT (www.itt.com). Space Engineering (www.space.it) Galileo Experimental Sensor Station antenna, Trimble (www.trimble.com) Zephyr Geodetic II antenna, and AOA D/MT antennas were used.

MICHAEL MEURER received a Ph.D. in electrical engineering from the University of Kaiserslautern, Germany. He is director of the Department for Navigation in the Institute for Communications and Navigation of the German Aerospace Center (DLR).

STEFAN ERKER received his diploma degree in information technology from the Technical University of Kaiserslautern and works at DLR’s Institute for Communications and Navigation.

STEFFEN THÖLERT received his diploma degree in electrical engineering from the University of Magdeburg and works at DLR.

OLIVER MONTENBRUCK works at DLR’s German Space Operations Center, Oberpfaffenhofen, where he is head of the GPS Technology and Navigation Group. He holds a Dr.rer.nat degree in physics.

ANDRÉ HAUSCHILD received his diploma degree in mechanical engineering from the Technical University of Braunschweig, Germany, and is a Ph.D. candidate at DLR’s German Space Operations Center.

Further Reading

L5 Signal Details
Interface Specification, IS-GPS-705 (IRN-705-003), Navstar GPS Space Segment/User Segment L5 Interfaces, ARINC Engineering Services, LLC, El Segundo, California, September 22, 2005.
“The New L5 Civil GPS Signal” by A.J. Van Dierendonck and C. Hegarty in GPS World, Vol. 11, No.9, September 2000, pp. 64–72.

DLR’s GNSS Verification and Analysis Facility
“GNSS Signal Verification: Spectral and Temporal Analysis of GIOVE B and BEIDOU Signals” by S. Thölert, S. Erker, M. Cuntz, M. Meurer, U. Grunert, and J. Furthner, presented at Navitec 2008, the 4th ESA Workshop on Satellite Navigation User Equipment Technologies, Noordwijk, The Netherlands, December 10–12, 2008.
“GNSS Signal Verification with a High Gain Antenna – Calibration Strategies and High Quality Signal Assessment” by S. Thölert, S. Erker, and M. Meurer in Proceedings of ITM 2009, the 2009 International Technical Meeting of The Institute of Navigation, Anaheim, California, January 26–28, 2009, pp. 289-300.

Nonlinearities in Microwave Signal Components
“Frequency-independent and Frequency Dependent Nonlinear Models of TWT Amplifiers” by A. Saleh in IEEE Transactions on Communications, Vol. 29, November 1981, pp. 1715–1720.
“Analysis of GIOVE-A L1-Signals” by S. Graf and C. Günther in Proceedings of ION GNSS 2006, the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 26-29, 2006, pp. 1560–1566.

Commercial GNSS Receivers Used for L5 Signal Acquisition
“Triumph Technology” by J. Ashjaee presented at the 5th Allsat Open Conference, Hannover, Germany, June 19, 2008.
“A Dual-frequency L1/E5a Galileo Test Receiver” by N. Gerein, M. Olynik, M. Clayton, J. Auld, and T. Murfin in Proceedings of the European Navigation Conference – GNSS 2005, Munich, Germany, July 19-22, 2005.

The Multipath Observable
“TEQC: The Multi-Purpose Toolkit for GPS/GLONASS Data” by L.H. Estey and C.M. Meertens in GPS Solutions, Vol. 3, No. 1, 1999, pp. 42–49.

1995 Reports on the Future of GPS
The Global Positioning System: Charting the Future: Charting the Future by a panel of the National Academy of Public Administration and by a committee of the National Research Council, National Academy of Public Administration, Washington, D.C., 1995, ISBN 0-9646874-1-0.
The Global Positioning System: A Shared National Asset, Recommendations for Technical Improvements and Enhancements by the National Research Council Committee on the Future of the Global Positioning System, National Academy Press, Washington, D.C., 1995, ISBN 0-309-05283-1.

The Seminal Article on the Benefits of Three GPS Signal Frequencies
“The Promise of a Third Frequency” by R.R. Hatch in GPS World, Vol. 7, No. 5, May 1996, pp. 55–58.

June 1, 2009
Innovation: GNSS antennas
An Introduction to Bandwidth, Gain Pattern, Polarization and All That

How do you find best antenna for particular GNSS application, taking into account size, cost, and capability? We look at the basics of GNSS antennas, introducing the various properties and trade-offs that affect functionality and performance. Armed with this information, you should be better able to interpret antenna specifications and to select the right antenna for your next job.

By Gerald J. K. Moernaut and Daniel Orban

INNOVATION INSIGHTS by Richard Langley

The antenna is a critical component of a GNSS receiver setup. An antenna’s job is to capture some of the power in the electromagnetic waves it receives and to convert it into an electrical current that can be processed by the receiver. With very strong signals at lower frequencies, almost any kind of antenna will do. Those of us of a certain age will remember using a coat hanger as an emergency replacement for a broken AM-car-radio antenna. Or using a random length of wire to receive shortwave radio broadcasts over a wide range of frequencies. Yes, the higher and longer the wire was the better, but the length and even the orientation weren’t usually critical for getting a decent signal.

Not so at higher frequencies, and not so for weak signals. In general, an antenna must be designed for the particular signals to be intercepted, with the center frequency, bandwidth, and polarization of the signals being important parameters in the design. This is no truer than in the design of an antenna for a GNSS receiver.

The signals received from GNSS satellites are notoriously weak. And they can arrive from virtually any direction with signals from different satellites arriving simultaneously. So we don’t have the luxury of using a high-gain dish antenna to collect the weak signals as we do with direct-to-home satellite TV.

Of course, we get away with weak GNSS signals (most of the time) by replacing antenna gain with receiver-processing gain, thanks to our knowledge of the pseudorandom noise spreading codes used to transmit the signals. Nevertheless, a well-designed antenna is still important for reliable GNSS signal reception (as is a low-noise receiver front end). And as the required receiver position fix accuracy approaches centimeter and even sub-centimeter levels, the demands on the antenna increase, with multipath suppression and phase-center stability becoming important characteristics.

So, how do you find the best antenna for a particular GNSS application, taking into account size, cost, and capability? In this month’s column, we look at the basics of GNSS antennas, introducing the various properties and trade-offs that affect functionality and performance. Armed with this information, you should be better able to interpret antenna specifications and to select the right antenna for your next job.

“Innovation” is a regular column that features discussions about recent advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering at the University of New Brunswick, who welcomes your comments and topic ideas. To contact him, see the “Contributing Editors” section.

The antenna is often given secondary consideration when installing or operating a Global Navigation Satellite Systems (GNSS) receiver. Yet the antenna is crucial to the proper operation of the receiver. This article gives the reader a basic understanding of how a GNSS antenna works and what performance to look for when selecting or specifying a GNSS antenna.

We explain the properties of GNSS antennas in general, and while this discussion is valid for almost any antenna, we focus on the specific requirements for GNSS antennas. And we briefly compare three general types of antennas used in GNSS applications.

When we talk about GNSS antennas, we are typically talking about GPS antennas as GPS has been the navigation system for years, but other systems have been and are being developed. Some of the frequencies used by these other systems are unique, such as Galileo’s E6 band and the GLONASS L1 band, and may not be covered by all antennas. But other than frequency coverage, all GNSS antennas share the same properties.

GNSS Antenna Properties

A number of important properties of GNSS antennas affect functionality and performance, including:
- Frequency coverage
- Gain pattern
- Circular polarization
- Multipath suppression
- Phase center
- Impact on receiver sensitivity
- Interference handling
We will briefly discuss each of these properties in turn.

Frequency Coverage. GNSS receivers brought to market today may include frequency bands such as GPS L5, Galileo E5/E6, and the GLONASS bands in addition to the legacy GPS bands, and the antenna feeding a receiver may need to cover some or all of these bands.

TABLE 1 presents an overview of the frequencies used by the various GNSS constellations. Keep in mind that you may see slightly different numbers published elsewhere depending on how the signal bandwidths are defined.

TABLE 1. GNSS Frequency Allocations. (Data: Gerald J. K. Moernaut and Daniel Orban)

As the bandwidth requirement of an antenna increases, the antenna becomes harder to design, and developing an antenna that covers all of these bands and making it compliant with all of the other requirements is a challenge.

If small size is also a requirement, some level of compromise will be needed.

Gain Pattern. For a transmitting antenna, gain is the ratio of the radiation intensity in a given direction to the radiation that would be obtained if the power accepted by the antenna was radiated isotropically. For a receiving antenna, it is the ratio of the power delivered by the antenna in response to a signal arriving from a given direction compared to that delivered by a hypothetical isotropic reference antenna. The spatial variation of an antenna’s gain is referred to as the radiation pattern or the receiving pattern. Actually, under the antenna reciprocity theorem, these patterns are identical for a given antenna and, ignoring losses, can simply be referred to as the gain pattern.

The receiver operates best with only a small difference in power between the signals from the various satellites being tracked and ideally the antenna covers the entire hemisphere above it with no variation in gain. This has to do with potential cross-correlation problems in the receiver and the simple fact that excessive gain roll-off may cause signals from satellites at low elevation angles to drop below the noise floor of the receiver.

On the other hand, optimization for multipath rejection and antenna noise temperature (see below) require some gain roll-off.

FIGURE 1. Theoretical antenna with hemispherical gain pattern. Boresight corresponds to θ = 0°. (Data: Gerald J. K. Moernaut and Daniel Orban)

FIGURE 1 shows what a perfect hemispherical gain pattern looks like, with a cut through an arbitrary azimuth.

However, such an antenna cannot be built and “real-world” GNSS antennas see a gain roll-off of 10 to 20 dB from boresight (looking straight up from the antenna) to the horizon. FIGURE 2 shows what a typical gain pattern looks like as a cross-section through an arbitrary azimuth.

FIGURE 2. “Real-world” antenna gain pattern. (Data: Gerald J. K. Moernaut and Daniel Orban)

Circular Polarization. Spaceborne systems at L-Band typically use circular polarization (CP) signals for transmitting and receiving. The changing relative orientation of the transmitting and receiving CP antennas as the satellites orbit the Earth does not cause polarization fading as it does with linearly polarized signals and antennas. Furthermore, circular polarization does not suffer from the effects of Faraday rotation caused by the ionosphere. Faraday rotation results in an electromagnetic wave from space arriving at the Earth’s surface with a different polarization angle than it would have if the ionosphere was absent. This leads to signal fading and potentially poor reception of linearly polarized signals.

Circularly polarized signals may either be right-handed or left-handed. GNSS satellites use right-hand circular polarization (RHCP) and therefore a GNSS antenna receiving the direct signals must also be designed for RHCP.

Antennas are not perfect and an RHCP antenna will pick up some left-hand circular polarization (LHCP) energy. Because GPS and other GNSS use RHCP, we refer to the LHCP part as the cross-polar component (see FIGURE 3).

FIGURE 3. Co- and cross-polar gain pattern versus boresight angle of a rover antenna. (Data: Gerald J. K. Moernaut and Daniel Orban)

We can describe the quality of the circular polarization by either specifying the ratio of this cross-polar component with respect to the co-polar component (RHCP to LHCP), or by specifying the axial ratio (AR). AR is the measure of the polarization ellipticity of an antenna designed to receive circularly polarized signals. An AR close to 1 (or 0 dB) is best (indicating a good circular polarization) and the relationship between the co-/cross-polar ratio and axial ratio is shown in FIGURE 4.

FIGURE 4. Converting axial ratio to co-/cross-polar ratio. (Data: Gerald J. K. Moernaut and Daniel Orban)

FIGURE 5. Co-/cross-polar and axial ratios versus boresight angle of a rover-style antenna. (Data: Gerald J. K. Moernaut and Daniel Orban)

FIGURE 5 shows the ratio of the co- and cross-polar components and the axial ratio versus boresight (or depression) angle for a typical GPS antenna. The boresight angle is the complement of the elevation angle.

For high-end GNSS antennas such as choke-ring and other geodetic-quality antennas, the typical AR along the boresight should be not greater than about 1 dB. AR increases towards lower elevation angles and you should look for an AR of less than 3 to 6 dB at a 10° elevation angle for a high-performance antenna. Expect to see small (<1 dB) variations of AR versus azimuth at the low elevation angles.

Maintaining a good AR over the entire hemisphere and at all frequencies requires a lot of surface area in the antenna and can only be accomplished in high-end antennas like base station and rover antennas.

Multipath Suppression. Signals coming from the satellites arrive at the GNSS receiver’s antenna directly from space, but they may also be reflected off the ground, buildings, or other obstacles and arrive at the antenna multiple times and delayed in time. This is termed multipath. It degrades positioning accuracy and should be avoided. High-end receivers are able to suppress multipath to a certain extent, but it is good engineering practice to suppress multipath in the antenna as much as possible.

A multipath signal can come from three basic directions:
- The ground and arrive at the back of the antenna.
- The ground or an object and arrive at the antenna at a low elevation angle.
- An object and arrive at the antenna at a high elevation angle.
Reflected signals typically contain a large LHCP component. The technique to mitigate each of these is different and, as an example, we will describe suppression of multipath signals due to ground and vertical object reflections.

Multipath susceptibility of an antenna can be quantified with respect to the antenna’s gain pattern characteristics by the multipath ratio (MPR). FIGURE 6 sketches the multipath problem due to ground reflections.

FIGURE 6. Quantifying multipath caused by ground reflections. (Data: Gerald J. K. Moernaut and Daniel Orban)

We can derive this MPR formula for ground reflections:

The MPR for signals that are reflected from the ground equals the RHCP antenna gain at a boresight angle (θ) divided by the sum of the RHCP and LHCP antenna gains at the supplement of that angle.

Signals that are reflected from the ground require the antenna to have a good front-to-back ratio if we want to suppress them because an RHCP antenna has by nature an LHCP response in the anti-boresight or backside hemisphere. The front-to-back ratio is nominally the difference in the boresight gain and the gain in the anti-boresight direction. A good front-to-back ratio also minimizes ground-noise pick-up.

Similarly, an MPR formula can be written for signals that reflect against vertical objects. FIGURE 7 sketches this.

FIGURE 7. Quantifying multipath caused by vertical object reflections. (Data: Gerald J. K. Moernaut and Daniel Orban)

And the formula looks like this:

The MPR for signals that are reflected from vertical objects equals the RHCP antenna gain at a boresight angle (θ) divided by the sum of the RHCP and LHCP antenna gains at that angle.

Multipath signals from reflections against vertical objects such as buildings can be suppressed by having a good AR at those elevation angles from which most vertical object multipath signals arrive. This AR requirement is readily visible in the MPR formula considering these reflections are predominantly LHCP, and in this case MPR simply equals the co- to cross-polar ratio.

LHCP reflections that arrive at the antenna at high elevation angles are not a problem because the AR tends to be quite good at these elevation angles and the reflection will be suppressed. LHCP signals arriving at lower elevation angles may pose a problem because the AR of an antenna at low elevation angles is degraded in “real-world” antennas. It makes sense to have some level of gain roll-off towards the lower elevation angles to help suppress multipath signals. However, a good AR is always a must because gain roll-off alone will not do not it.

Phase Center. A position fix in GNSS navigation is relative to the electrical phase center of the antenna. The phase center is the point in space where all the rays appear to emanate from (or converge on) the antenna. Put another way, it is the point where the electromagnetic fields from all incident rays appear to add up in phase. Determining the phase center is important in GNSS applications, particularly when millimeter-positioning resolution is desired.

Ideally, this phase center is a single point in space for all directions at all frequencies. However, a “real-world” antenna will often possess multiple phase center points (for each lobe in the gain pattern, for example) or a phase center that appears “smeared out” as frequency and viewing angle are varied.

The phase-center offset can be represented in three dimensions where the offset is specified for every direction at each frequency band. Alternatively, we can simplify things and average the phase center over all azimuth angles for a given elevation angle and define it over the 10° to 90° elevation-angle range. For most applications even this simplified representation is over-kill, and typically only a vertical and a horizontal phase-center offset are specified for all bands in relation to L1.

For well-designed high-end GNSS antennas, phase center variations in azimuth are small and on the order of a couple of millimeters. The vertical phase offsets are typically 10 millimeters or less. Many high-end antennas have been calibrated, and tables of phase-center offsets for these antennas are available.

Impact on Receiver Sensitivity. The strength of the signals from space is on the order of -130 dBm. We need a really sensitive receiver if we want to be able to pick these up. For the antenna, this translates into the need for a high-performance low noise amplifier (LNA) between the antenna element itself and the receiver.

We can characterize the performance of a particular receiver element by its noise figure (NF), which is the ratio of actual output noise of the element to that which would remain if the element itself did not introduce noise. The total (cascaded) noise figure of a receiver system (a chain of elements or stages) can be calculated using the Friss formula as follows:

The total system NF equals the sum of the NF of the first stage (NF1) plus that of the second stage (NF2) minus 1 divided by the total gain of the previous stage (G1) and so on. So the total NF of the whole system pretty much equals that of the first stage plus any losses ahead of it such as those due to filters.

Expect to see total LNA noise figures in the 3-dB range for high performance GNSS antennas.

The other requirement for the LNA is for it to have sufficient gain to minimize the impact of long and lossy coaxial antenna cables — typically 30 dB should be enough. Keep in mind that it is important to have the right amount of gain for a particular installation. Too much gain may overload the receiver and drive it into non-linear behavior (compression), degrading its performance. Too little, and low-elevation-angle observations will be missed. Receiver manufacturers typically specify the required LNA gain for a given cable run.

Interference Handling. Even though GNSS receivers are good at mitigating some kinds of interference, it is essential to keep unwanted signals out of the receiver as much as possible. Careful design of the antenna can help here, especially by introducing some frequency selectivity against out-of-band interferers. The mechanisms by which in-band an out-of-band interference can create trouble in the LNA and the receiver and the approach to dealing with them are somewhat different.

FIGURE 8. Strong out-of-band interferer and third harmonic in the GPS L1 band. (Data: Gerald J. K. Moernaut and Daniel Orban)

An out-of-band interferer is generally an RF source outside the GNSS frequency bands: cellular base stations, cell phones, broadcast transmitters, radar, etc. When these signals enter the LNA, they can drive the amplifier into its non-linear range and the LNA starts to operate as a multiplier or comb generator. This is shown in FIGURE 8 where a -30-dBm-strong interferer at 525 MHz generates a -78 dBm spurious signal or spur in the GPS L1 band.

Through a similar mechanism, third-order mixing products can be generated whereby a signal is multiplied by two and mixes with another signal. As an example, take an airport where radars are operating at 1275 and 1305 MHz. Both signals double to 2550 and 2610 MHz. These will in turn mix with the fundamentals and generate 1245 and 1335 MHz signals.

Another mechanism is de-sensing: as the interference is amplified further down in the LNA’s stages, its amplitude increases, and at some point the GNSS signals get attenuated because the LNA goes into compression. The same thing may happen down the receiver chain. This effectively reduces the receiver’s sensitivity and, in some cases, reception will be lost completely.

RF filters can reduce out-of-band signals by 10s of decibels and this is sufficient in most cases. Of course, filters add insertion loss and amplitude and phase ripple, all of which we don’t want because these degrade receiver performance.

In-band interferers can be the third-order mixing products we mentioned above or simply an RF source that transmits inside the GNSS bands. If these interferers are relatively weak, the receiver will handle them, but from a certain power level on, there is just not a lot we can do in a conventional commercial receiver.

The LNA should be designed for a high intercept point (IP)–at which non-linear behavior begins–so compression does not occur with strong signals present at its input. On the other hand, there is no requirement for the LNA to be a power amplifier. As an example, let’s say we have a single strong continuous wave interferer in the L1 band that generates -50 dBm at the input of the LNA. A 50 dB, high IP LNA will generate a 0 dBm carrier in the L1 band but the receiver will saturate.

LNAs with a higher IP tend to consume more power and in a portable application with a rover antenna — that may be an issue. In a base-station antenna, on the other hand, low current consumption should not be a requirement since a higher IP is probably more valuable than low power consumption.

GNSS Antenna Types

Here is a short comparison of three types of GNSS antennas: geodetic, rover, and handheld. For detailed specifications of examples of each of these types, see the references in Further Reading.

Geodetic Antennas. High precision, fixed-site GNSS applications require geodetic-class receivers and antennas. These provide the user with the highest possible position accuracy.

As a minimum, typical geodetic antennas cover the GPS L1 and L2 bands. Some also cover the GLONASS frequencies. Coverage of L5 is found in some newer designs as well as coverage of the Galileo frequencies and the L-band frequencies of differential GNSS services.

The use of choke-ring ground planes is typical in geodetic antennas. These allow good gain pattern control, excellent multipath suppression, high front-to-back ratio, and good AR at low elevation angles. Choke rings contribute to a stable phase center. The phase center is documented (as mentioned earlier), and high-end receivers allow the antenna behavior to be taken into account. Combined with a state-of-the-art LNA, these antennas provide the highest possible performance.

Rover Antennas. Rover antennas are typically used in land survey, forestry, construction, and other portable or mobile applications. They provide the user with good accuracy while being optimized for portability. Horizontal phase-center variation versus azimuth should be low because the orientation of the antenna with respect to magnetic north, say, is usually unknown and cannot be corrected for in the receiver. A rover antenna is typically mounted on a handheld pole. Good front-to-back ratio is required to avoid operator-reflection multipath and ground-noise pickup. Yet these rover-type applications are high accuracy and require a good phase-center stability. However, since a choke ring cannot be used because of its size and weight, a higher phase-center variation compared to that of a geodetic antenna is typically inherent to the rover antenna design.

A good AR and a decent gain roll-off at low elevation angles ensures good multipath suppression as heavy choke rings are not an option for this configuration.

Handheld Receiver Antennas. These antennas are single-band L1 structures optimized for size and cost. They are available in a range of implementations, such as surface mount ceramic chip, helical, and patch antenna types. Their radiation patterns are quasi-hemispherical. AR and phase-center performance are a compromise because of their small size. Because of their reduced size, these antennas tend to have a negative gain of about -3 dBi (3 dB less than an ideal isotropic antenna) at boresight. This negative gain is mostly masked by an embedded LNA. The associated elevated noise figure is typically not an issue in handheld applications.

TABLE 2. Characteristics of different GNSS antenna classes. (Data: Gerald J. K. Moernaut and Daniel Orban)

Summary of Antenna Types. TABLE 2 presents a comparison of the most important properties of geodetic, rover, and handheld types of GNSS antennas.

Conclusion

In this article, we have presented an overview of the most important characteristics of GNSS antennas. Several GNSS receiver-antenna classes were discussed based on their typical characteristics, and the resulting specification compromises were outlined. Hopefully, this information will help you select the right antenna for your next GNSS application.

Acknowledgment

An earlier version of this article entitled “Basics of GPS Antennas” appeared in The RF & Microwave Solutions Update, an online publication of RF Globalnet.

GERALD J. K. MOERNAUT holds an M.Sc. degree in electrical engineering. He is a full-time antenna design engineer with Orban Microwave Products, a company that designs and produces RF and microwave subsystems and antennas with offices in Leuven, Belgium, and El Paso, Texas.

DANIEL ORBAN is president and founder of Orban Microwave Products. In addition to managing the company, he has been designing antennas for a number of years.

FURTHER READING

Previous GPS World Articles on GNSS Antennas

“Getting into Pockets and Purses: Antenna Counters Sensitivity Loss in Consumer Devices” by B. Hurte and O. Leisten in GPS World, Vol. 16, No. 11, November 2005, pp. 34-38.

“Characterizing the Behavior of Geodetic GPS Antennas” by B.R. Schupler and T.A. Clark in GPS World, Vol. 12, No. 2, February 2001, pp. 48-55.

“A Primer on GPS Antennas” by R.B. Langley in GPS World, Vol. 9, No. 7, July 1998, pp. 50-54.

“How Different Antennas Affect the GPS Observable” by B.R. Schupler and T.A. Clark in GPS World, Vol. 2, No. 10, November 1991, pp. 32-36.

Introduction to Antennas and Receiver Noise

“GNSS Antennas and Front Ends” in A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by K. Borre, D.M.Akos, N. Bertelsen, P. Rinder, and S.H. Jensen, Birkhäuser Boston, Cambridge, Massachusetts, 2007.

The Technician’s Radio Receiver Handbook: Wireless and Telecommunication Technology by J.J. Carr, Newnes Press, Woburn, Massachusetts, 2000.

“GPS Receiver System Noise” by R.B. Langley in GPS World, Vol. 8, No. 6, June 1997, pp. 40-45.

More on GNSS Antenna Types

“The Basics of Patch Antennas” by D. Orban and G.J.K. Moernaut. Available on the Orban Microwave Products website.

“Project Examples”

Interference in GNSS Receivers

“Interference Heads-Up: Receiver Techniques for Detecting and Characterizing RFI” by P.W. Ward in GPS World, Vol. 19, No. 6, June 2008, pp. 64-73.

“Jamming GPS: Susceptibility of Some Civil GPS Receivers” by B. Forssell and T.B. Olsen in GPS World, Vol. 14, No. 1, January 2003, pp. 54-58.
February 1, 2009
Innovation: Tsunami Detection by GPS
QuickBird satellite image of Kalutara Beach on the southwestern coast of Sri Lanka showing the receding waters and beach damage from the Sumatra tsunami.( Credit: Digital Globe)

How Ionospheric Observations Might Improve the Global Warning System

By Giovanni Occhipinti, Attila Komjathy, and Philippe Lognonné

Recent investigations have demonstrated that GPS might be an effective tool for improving the tsumani early-warning system through rapid determination of earthquake magnitude using data from GPS networks. A less obvious approach is to use the GPS data to look for the tsunami signature in the ionosphere.

INNOVATION INSIGHTS by Richard Langley

THE TSUNAMI generated by the December 26, 2004, earthquake just off the coast of the Indonesian island of Sumatra killed over 200,000 people. It was one of the worst natural disasters in recorded history. But it might have been largely averted if an adequate warning system had been in place.

A tsunami is generated when a large oceanic earthquake causes a rapid displacement of the ocean floor. The resulting ocean oscillations or waves, while only on the order of a few centimeters to tens of centimeters in the open ocean, can grow to be many meters even tens of meters when they reach shallow coastal areas. The speed of propagation of tsunami waves is slow enough, at about 600 to 700 kilometers per hour, that if they can be detected in the open ocean, there would be enough time to warn coastal communities of the approaching waves, giving people time to flee to higher ground.

Seismic instruments and models are used to predict a possible tsunami following an earthquake and ocean buoys and pressure sensors on the ocean bottom are used to detect the passage of tsunami waves. But globally, the density of such instrumentation is quite low and, coupled with the time lag needed to process the data to confirm a tsunami, an effective global tsunami warning system is not yet in place.

However, recent investigations have demonstrated that GPS might be a very effective tool for improving the warning system. This can be done, for example, through rapid determination of earthquake magnitude using data from existing GPS networks. And, incredible as it might seem, another approach is to use the GPS data to look for the tsunami signature in the ionosphere: the small displacement of the ocean surface displaces the atmosphere and makes it all the way to the ionosphere, causing measurable changes in ionospheric electron density.

In this month’s column, we look in detail at how a tsunami can affect the ionosphere and how GPS measurements of the effect might be used to improve the global tsunami warning system.

“Innovation” is a regular column that features discussions about recent advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering at the University of New Brunswick.

The December 26, 2004, earthquake-generated Sumatra tsunami caused enormous losses in life and property, even in locations relatively far away from the epicentral area. The losses would likely have never been so massive had an effective worldwide tsunami warning system been in place. A tsunami travels relatively slowly and it takes several hours for one to cross the Indian Ocean, for example. So a warning system should be able to detect a tsunami and provide an alert to coastal areas in its path. Among the strengths of a tsunami early-warning system would be its capability to provide an estimate of the magnitude and location of an earthquake. It should also confirm the amplitude of any associated tsunami, due to massive displacement of the ocean bottom, before it reaches populated areas. In the aftermath of the Sumatra tsunami, an important effort is underway to interconnect seismic networks and to provide early alarms quantifying the level of tsunami risk within 15 minutes of an earthquake.

However, the seismic estimation process cannot quantify the exact amplitude of a tsunami, and so the second step, that of tsunami confirmation, is still a challenge. The earthquake fault mechanism at the epicenter cannot fully explain the initiation of a tsunami as it is only approximated by the estimated seismic source. The fault slip is not transmitted linearly at the ocean bottom due to various factors including the effect of the bathymetry, the fault depth, and the local lithospheric properties as well as possible submarine landslides associated with the earthquake.

In the open ocean, detecting, characterizing, and imaging tsunami waves is still a challenge. The offshore vertical tsunami displacement (on the order of a few centimeters up to half a meter in the case of the Sumatra tsunami) is hidden in the natural ocean wave fluctuations, which can be several meters or more. In addition, the number of offshore instruments capable of tsunami measurements, such as tide gauges and buoys, is very limited. For example, there are only about 70 buoys in the whole world. As a tsunami propagates with a typical speed of 600–700 kilometers per hour, a 15-minute confirmation system would require a worldwide buoy network with a 150-kilometer spacing.

Satellite altimetry has recently proved capable of measuring the sea surface variation in the case of large tsunamis, including the December 2004 Sumatra event. However, satellites only supply a few snapshots along the sub-satellite tracks. Optical imaging of the shore hs successfully measured the wave arrival at the coastline (see ABOVE PHOTO), but it is ineffective in the open sea. At present, only ocean-bottom sensors and GPS buoy receivers supply measures of mid-ocean vertical displacement. In many cases, the tsunami can only be identified several hours after the seismic event due to the poor distribution of sensors. This delay is necessary for the tsunami to reach the buoys and for the signal to be recorded for a minimum of one wave period (a typical tsunami wave period is between 10 and 40 minutes) to be adequately filtered by removing the “noise” due to normal wave action.

In the case of the December 2004 Sumatra event, the first tsunami measurements by any instrumentation were only made available about 3 hours after the earthquake. They were supplied by the real-time tide gauge at the Cocos Islands, an Australian territory in the southeast Indian Ocean (see FIGURE 1 where the tsunami signature is superimposed on the large semidiurnal tide fluctuation). Up until that time, the tsunami could not be fully confirmed and coastal areas remained vulnerable to tsunami damage. This delay in confirmation is a fundamental weakness of the existing tsunami warning systems.

Figure 1. The Sumatra tsunami signal measured at the Cocos Islands by the tide gauge (red) and by the co-located GPS receiver (blue). The tide gauge measures the sea-level displacement (tide plus superimposed tsunami) and the GPS receiver measures the slant total electron content perturbation (+/-1 TEC unit) in the ionosphere.

Ionospheric Perturbation. Recently, observational and modeling results have confirmed the existence and detectability of a tsunamigenic signature in the ionosphere. Physically, the displacement induced by tsunamis at the sea surface is transmitted into the atmosphere where it produces internal gravity waves (IGWs) propagating upward. (When a fluid or gas parcel is displaced at an interface, or internally, to a region with a different density, gravity restores the parcel toward equilibrium resulting in an oscillation about the equilibrium state; hence the term gravity wave.) The normal ocean surface variability has a typical high frequency (compared to tsunami waves) and does not transfer detectable energy into the atmosphere. In other words, the Earth’s atmosphere behaves as an “analog low-pass filter.” Only a tsunami produces propagating waves in the atmosphere. During the upward propagation, these waves are strongly amplified by the double effects of the conservation of kinetic energy and the decrease of atmospheric density resulting in a local displacement of several tens of meters per second at 300 kilometers altitude in the atmosphere. This displacement can reach a few hundred meters per second for the largest events.

At an altitude of about 300 kilometers, the neutral atmosphere is strongly coupled with the ionospheric plasma producing perturbations in the electron density. These perturbations are visible in GPS and satellite altimeter data since those signals have to transit the ionosphere. The dual-frequency signal emitted by GPS satellites can be processed to obtain the integral of electron density along the paths between the satellites and the receiver, the total electron content (TEC).

Within about 15 minutes, the waves generated at the sea surface reach ionospheric altitudes, creating measurable fluctuations in the ionospheric plasma and consequently in the TEC. This indirect method of tsunami detection should be helpful in ocean monitoring, allowing us to follow an oceanic wave from its generation to its propagation in the open ocean.

So, can ionospheric sounding provide a robust method of tsunami confirmation? It is our hope that in the future this technique can be incorporated into a tsunami early-warning system and complement the more traditional methods of detection including tide gauges and ocean buoys. Our research focuses on whether ground-based GPS TEC measurements combined with a numerical model of the tsunami-ionosphere coupling could be used to detect tsunamis robustly. Such a detection scheme depends on how the ionospheric signature is related to the amplitude of the sea surface displacement resulting from a tsunami. In the near future, the ionospheric monitoring of TEC perturbations might become an integral part of a tsunami warning system that could potentially make it much more effective due to the significantly increased area of coverage and timeliness of confirmation.

In this article, we’ll take a look at the current state of the art in modeling tsunami-generated ionospheric perturbations and the status of attempts to monitor those perturbations using GPS.

Some Background

Pioneering work by the Canadian atmospheric physicist Colin Hines in the 1970s suggested that tsunami-related IGWs in the atmosphere over the oceanic regions, while interacting with the ionospheric plasma, might produce signatures detectable by radio sounding.

In June 2001, an episodic perturbation was observed following a tsunamigenic earthquake in Peru. After its propagation across the Pacific Ocean (taking about 22 hours), the tsunami reached the Japanese coast and its signature in the ionosphere was detected by the Japanese GPS dense network (GEONET). The perturbation, shown in FIGURE 2, has an arrival time and characteristic period consistent with the tsunami propagation determined from independent methods. Unfortunately, similar signatures in the ionosphere are also produced by IGWs associated with traveling ionospheric disturbances (TIDs), and are commonly observed in the TEC data. However, the known azimuth, arrival time, and structure of the tsunami allows us to use this data source, even if it contains background TIDs.

Figure 2. The observed signal for the June 23, 2001, tsunami (initiated offshore Peru). Total electron content variations are plotted at the ionosphere pierce points. A wave-like disturbance is seen propagating toward the coast of Honshu, the main island of Japan.

The December 26, 2004, Sumatra earthquake, with a magnitude of 9.3, was an order of magnitude larger than the Peru event and was the first earthquake and tsunami of magnitude larger than 9 of the so-called “human digital era,” comparable to the magnitude 9.5 Chilean earthquake of May 22, 1960.

In addition to seismic waves registered by global seismic networks, the Sumatra event produced infragravity waves (long-period wave motions with typical periods of 50 to 200 seconds) remotely observed from the island of Diego Garcia, perturbations in the magnetic field observed by the CHAMP satellite, and a series of ionospheric anomalies.

Two types of ionospheric anomaly were observed: anomalies of the first type, detected worldwide in the first few hours after the earthquake, were reported from north of Sumatra, in Europe, and in Japan. They are associated with the surface seismic waves that propagate around the world after an earthquake rupture (so-called Rayleigh waves).

Anomalies of the second type were detected above the ocean and were clearly associated with the tsunami. In the Indian Ocean, the occurrence times of TEC perturbations observed using ground-based GPS receivers and satellite altimeters were consistent with the observed tsunami propagation speed. The GPS observations from sites to the north of Sumatra show internal gravity waves most likely coupled with the tsunami or generated at the source and propagating independently in the atmosphere. The link with the tsunami is more evident in the observations elsewhere in the Indian Ocean. The TEC perturbations observed by the other ground-based GPS receivers moved horizontally with a velocity coherent with the tsunami propagation.

Figure 3. The tsunamigenic earthquake mechanism and transfer of energy in the neutral and ionized atmosphere. The solid Earth displacement produces the tsunami and the sea surface displacement produces an internal gravity wave in the neutral atmosphere, which perturbs the electron distribution in the ionosphere.

The amplitude of the observed TEC perturbations is strongly dependent on the filter method used. The four TECU-level peak-to-peak variations in filtered GPS TEC measurements from north of Sumatra are coherent with the differential TEC at the 0.4 TECU per 30 seconds level observed in the rest of the Indian Ocean. (One TEC unit or TECU is 10¹⁶ electrons per meter-squared, equivalent to 0.162 meters of range delay at the GPS L1 frequency.) Such magnitudes can be detected using GPS measurements since GPS phase observables are sensitive to TEC fluctuations at the 0.01 TECU level. We emphasize also the role of the elevation angle in the detection of tsunamigenic perturbations in the ionosphere. As a consequence of the integrated nature of TEC and the vertical structure of the tsunamigenic perturbation, low-elevation angle geometry is more sensitive to the tsunami signature in the GPS data, hence it is more visible.

The TEC perturbation observed at the Cocos Islands by GPS can be compared with the co-located tide-gauge (Figure 1). The tsunami signature in the data from the two different instruments shows a similar waveform, confirming the sensitivity of the ionospheric measurement to the tsunami structure.

The link between the tsunami at sea level and the perturbation observed in the ionosphere has been demonstrated using a 3D numerical modeling based on the coupling between the ocean surface, the neutral atmosphere, and the ionosphere (see FIGURE 3). The modeling reproduced the TEC data with good agreement in amplitude as well as in the waveform shape, and quantified it by a cross-correlation (see FIGURE 4). The resulting shift of +/-1 degree showed the presence of zonal and meridional winds neglected in the modeling. The presence of the wind can, indeed, introduce a shift of 1 degree in latitude and 1.5 degrees in longitude.

Since modeling is an effective method to discriminate between the tsunami signature in the ionosphere and other potential perturbations, the GPS observations can be a useful tool to develop an inexpensive tsunami detection system based on the ionospheric sounding.

Figure 4. Satellite altimeter and total electron content (TEC) signatures of the Sumatra tsunami. The modeled and observed TEC is shown for (a) Jason-1 and for (b) Topex/Poseidon: data (black), synthetic TEC without production-recombination-diffusion effects (blue), with production-recombination (red), and production-recombination-diffusion (green). The Topex/Poseidon synthetic TEC has been shifted up by 2 TEC units. In (c) and (d), the altimetric measurements of the ocean surface (black) are plotted for the Jason-1 and Topex/Poseidon satellites, respectively. The synthetic ocean displacement, used as the source of internal gravity waves in the neutral atmosphere, is shown in red. In (e), the cross-correlations between TEC synthetics and data are shown for Jason-1 (blue) and Topex/Poseidon (red).

Modeling TEC Perturbations

A model to describe the effect of a tsunami on the ionosphere has been developed at the Institut de Physique du Globe de Paris (IPGP), France. It is comprised of three main parts. Firstly, it computes tsunami propagation using realistic bathymetry of, for example, the Indian Ocean. Secondly, an oceanic displacement is used to excite IGWs in the neutral atmosphere. Thirdly, it computes the response of the ionosphere induced by the neutral atmospheric motion resulting in enhanced electron densities. After integrating the electron densities, we obtain modeled (synthetic) TEC data. The modeling steps are as follows:

Tsunami Propagation. Tsunami modeling is an established science and the propagation of tsunamis is generally based on a shallow-water hypothesis. Under this hypothesis, the ocean is considered as a simple layer where the ocean depth, h, is locally taken into account in the tsunami propagation velocity, v = √ hg, which directly depends on h and the gravity acceleration g. The modeling, usually based on finite differences, solves the appropriate hydrodynamic equations.

Neutral Atmosphere Coupling. A tsunami is an oceanic gravity wave and its propagation is not limited to the oceanic surface; as previously discussed, the ocean displacement is transferred to the atmosphere where it becomes an internal gravity wave. This coupling phenomenon is linear and can be reproduced solving the wave propagation equations, nominally the continuity and the so-called Navier-Stokes equations. These equations are solved assuming the atmosphere to be irrotational, inviscid, and incompressible. The IGWs are, indeed, imposed by displacement of the mass under the effect of the gravity force, contrary to the elastic waves generated by compression (for example, sound waves), so the medium can be considered incompressible. FIGURE 5 shows the IGWs produced by the Sumatra tsunami. The inversion of the velocity with altitude (wind shear) is a typical structure of IGWs.

Neutral-Plasma Coupling. The tsunamigenic IGWs are injected into a 3D ionospheric model to reproduce the induced electron density perturbations. In essence, the coupling model solves the hydromagnetic equations for three ion species (O2 ⁺ , NO⁺ , and O⁺ ). Physically, the neutral atmosphere motion induces fluctuations in the plasma velocity by way of momentum transfer driven by collision frequency and the Lorentz term associated with Earth’s magnetic and electric fields. Ion loss, recombination, and diffusion are also taken into account in the ion continuity equation. Finally, the perturbed electron density is inferred from ion densities using the charge neutrality hypothesis. The International Reference Ionosphere model is used for background electron density; SAMI2 (a recursive acronym: SAMI2 is Another Model of the Ionosphere) is used for collision, production, and loss parameters; and a constant geomagnetic field is assumed based on the International Geomagnetic Reference Field. FIGURE 5 shows the perturbation induced in the ionospheric plasma by the tsunamigenic IGW following the Sumatra event. The perturbation is strongly localized to around 300 kilometers altitude where the electron density background is maximized.

Figure 5. Internal gravity waves (IGWs) generated by the Sumatra tsunami and the response of the ionosphere to neutral motion at 02:40 UT (almost two hours after the earthquake). On the left, the normalized vertical velocity induced by tsunami-generated IGWs in the neutral atmosphere is shown. On the right, the perturbation induced by IGWs in the ionospheric plasma (in electrons per cubic meter) is shown, with the maximum perturbation at an altitude of about 300 kilometers. The vertical cut shown in these profiles is at a latitude of -1 degree.

The resulting electron density dynamic model described above allows us to compute a map of the perturbed TEC by simple vertical integration (see FIGURE 6). In addition to the geometrical dispersion of the tsunami, the TEC map shows horizontal heterogeneities in the electron density perturbation that are induced by the geomagnetic field inclination. The magnetic field plays a fundamental role in the neutral-plasma coupling, resulting in a strong amplification at the magnetic equator where the magnetic field is directed horizontally. The isolated perturbation appearing more to the south is probably induced by the full development of the IGW in the atmosphere. Recent work also explains this second perturbation as induced by the role of the magnetic field in the neutral-plasma coupling.

Figure 6. The signature of the Sumatra tsunami in total electron content (TEC) at 03:18 UT (right) compared with the unperturbed TEC (left). The TEC images have been computed by vertical integration of the perturbed and unperturbed electron density fields. The broken lines represent the Topex/Poseidon (left) and Jason-1 (right) trajectories. The blue contours represent the geomagnetic field inclination.

GPS Data Processing

To validate our model, we use ground-based GPS receivers to look for the ionospheric signal induced by tsunamis. Prior research has shown post-processed results detecting a tsunami-generated TEC signal using regional GPS networks such as GEONET in Japan (about 1,000 stations) or the Southern California Integrated GPS Network (about 200 stations). Those studies benefited from the very high density of GPS receivers in the regional networks, so that, for example, no forward modeling was needed to help initially identify the characteristics of the tsunami-generated signal.

High-Precision Processing. More than 1,300 globally-distributed dual-frequency GPS receivers are available using publicly accessible networks, including those of the International GNSS Service and the Continuously Operating GPS Stations coordinated by the U.S. National Geodetic Survey. Most researchers estimate vertical ionospheric structure and, simultaneously, treat hardware-related biases as nuisance parameters. In our approach for calibrating GPS receiver and satellite inter-frequency biases, we take advantage of all available GPS receivers using a new processing technique based on the Global Ionospheric Mapping software developed at the Jet Propulsion Laboratory (JPL). FIGURE 7 shows a JPL TEC map using 1,000 GPS stations. This new capability is designed to estimate receiver biases for all stations in the global network. We solve for the instrumental biases by modeling the ionospheric delay and removing it from the observation.

Figure 7. The total electron content (TEC) between 01:00 and 01:15 UT on December 26, 2004, at ionosphere pierce points (IPPs) provided by a global network of more than 1,000 GPS tracking stations. To highlight variations, a five-day average of TEC has been subtracted from the observed TEC.

Ionospheric Warning System

The currently implemented tsunami warning system uses seismometers to detect earthquakes and to perform an estimation of the seismic moment by monitoring seismic waves. After a potential tsunami risk is determined, ocean buoy and pressure sensors have to confirm the tsunami risk. Unfortunately, the number of available ocean buoys is limited to about 70 over the whole planet. With the existing system, it may take several hours to confirm a tsunami when taking into account both the propagation time (of tsunamis reaching buoys) and data-processing time. On the other hand, the proposed ionosphere-based tsunami detection system may only require the propagation time and data-processing delays of only up to about 15–30 minutes. GPS receivers are able to sound the ionosphere up to about 20 degrees away from the receiver location, and a dense GPS network can therefore increase the coverage of the monitored area.

The fundamental idea behind a detection method is that we need to separate tsunami-generated TEC signatures from other sources of ionospheric disturbances. However, the tsunami-generated TEC perturbations are distinguishable because they are tied to the propagation characteristics of the tsunami. Tsunami-related fluctuations should be in the gravity-wave period domain and cohere in geometry and distance with the earthquake epicenter (for example, they show up in data on multiple satellites from multiple stations and, with increasing distance from the epicenter, at a rate related to tsunami propagation speed).

The coupled tsunami model described earlier can also be used to compute a prediction for the tsunami-generated TEC perturbation based on the seismic displacement as an input parameter to the model. The model prediction may be used as a detection aid by indicating the location of the tsunami wave front with time. This permits us to focus our detection efforts on specific locations and times, and will allow us to discriminate signal from noise.

The model also provides information on the expected magnitude of the TEC perturbation. This provides further value in filter discrimination. Cross-correlations can be performed on nearby observations using different satellites and stations to take advantage of tsunami-related perturbations being coherent in geometry and distance from the epicenter. Once the signal is detected in data from multiple satellites and stations, we can “track” and image the tsunami during its propagation in space and time.

The goal of our research is to assess the feasibility of detecting tsunamis in near real time. This requires that GPS data be acquired rapidly. Rapid availability of ground-based GPS data has been demonstrated via the NASA Global Differential GPS System, a highly accurate, robust real-time GPS monitoring and augmentation system.

Conclusions

Earlier research using GPS-derived TEC observations has revealed TEC perturbations induced by tsunamis. However, in our research, we use a combination of a coupled ionosphere-atmosphere-tsunami model with large GPS data sets. Ground-based GPS data are used to distinguish tsunami-generated TEC perturbations from background fluctuations. Tsunamis are among the most disrupting forces humankind faces. The December 26, 2004, earthquake and resulting tsunami claimed more than 200,000 lives, with several hundreds of thousands of people injured. The damage in infrastructure and other economic losses were estimated to be in the range of tens of billions of dollars. To help prevent such a global disaster from occurring again, we suggest that ionospheric sounding by GPS be integrated into the existing tsunami warning system as soon as possible.

Acknowledgments

This article is based on the paper “Three-Dimensional Waveform Modeling of Ionospheric Signature Induced by the 2004 Sumatra Tsunami” published in Geophysical Research Letters. The authors wish to acknowledge François Crespon (Noveltis, Ramonville-Saint-Agne, France) for the TEC data analysis in Figure 1, Juliette Artru (Centre National d’Etudes spatiales – CNES, Toulouse, France) for her work on the detection of tsunamigenic TEC perturbations shown in this article, and Grégoire Talon for Figure 3. The IPGP portion of the work is sponsored by L’Agence Nationale de la Recherche, by CNES, and by the Ministère de l’Enseignement supérieur et de la Recherche. The first author would also like to thank John LaBrecque of NASA’s Science Mission Directorate for supporting his fellowship at the California Institute of Technology/JPL.

GIOVANNI OCCHIPINTI received his Ph.D. at the Institut de Physique du Globe de Paris (IPGP) in 2006. In 2007, he joined NASA’s Jet Propulsion Laboratory (JPL), California Institute of Technology, as a postdoctoral fellow to continue his work on the detection and modeling of tsunamigenic perturbations in the ionosphere. He will soon take up the position of assistant professor at the University of Paris and IPGP. His scientific interests are focused on solid Earth-atmosphere-ionosphere coupling.

ATTILA KOMJATHY is senior staff member of the Ionospheric and Atmospheric Remote Sensing Group of Tracking Systems and Applications Section at JPL, specializing in remote sensing techniques. He received his Ph.D. from the Department of Geodesy and Geomatics Engineering at the University of New Bruns-wick, Canada, in 1997. He has received the Canadian Governor General’s Gold Medal for Academic Excellence and NASA awards including an Exceptional Space Act Award.

PHILIPPE LOGNONNÉ is the director of the Space Department of IPGP, a professor at the University of Paris VII, and a junior member of the Institut Universitaire de France. His science interests are in the field of remote sensing and are related to the detection of seismic waves and tsunamis in the ionosphere. Also, he participates in several projects in planetary seismology.

FURTHER READING
- Ionospheric Seismology
“3D Waveform Modeling of Ionospheric Signature Induced by the 2004 Sumatra Tsunami” by G. Occhipinti, P. Lognonné, E. Alam Kherani, and H. Hebert, in Geophysical Research Letters, Vol. 33, L20104, doi:10.1029/2006GL026865, 2006.

“Ground-based GPS Imaging of Ionospheric Post-seismic Signal” by P. Lognonné, J. Artru, R. Garcia, F. Crespon, V. Ducic, E. Jeansou, G. Occhipinti, J. Helbert, G. Moreaux, and P.E. Godet in Planetary and Space Science, Vol. 54, No. 5, April 2006, pp. 528–540.

“Tsunamis Detection in the Ionosphere” by J. Artru, P. Lognonné, G. Occhipinti, F. Crespon, R. Garcia, E. Jeansou, and M. Murakami in Space Research Today, Vol. 163, 2005, pp. 23–27.

“On the Possible Detection of Tsunamis by a Monitoring of the Ionosphere” by W.R. Peltier and C.O. Hines in Journal of Geophysical Research, Vol. 81, No. 12, 1976, pp. 1995–2000.

Space and Planetary Geophysics Laboratory at the IPGP.
- Ionospheric Effects on GPS
“Unusual Topside Ionospheric Density Response to the November 2003 Superstorm” by E. Yizengaw, M.B. Moldwin, A. Komjathy, and A.J. Mannucci in Journal of Geophysical Research, Vol. 111, A02308, doi:10.1029/2005JA011433, 2006.

“Automated Daily Processing of More than 1000 Ground-based GPS Receivers for Studying Intense Ionospheric Storms” by A. Komjathy, L. Sparks, B.D. Wilson, and A.J. Mannucci in Radio Science, Vol. 40, RS6006, doi:10.1029/2005RS003279, 2005.

“Space Weather: Monitoring the Ionosphere with GPS” by A. Coster, J. Foster, and P. Erickson in GPS World, Vol. 14, No. 5, May 2003, pp. 42–49.

“GPS, the Ionosphere, and the Solar Maximum” by R.B. Langley in GPS World, Vol. 11, No. 7, July 2000, pp. 44–49.
- Real-time GPS Data Collection and Dissemination
NASA Global Differential GPS System
February 1, 2008
Innovation: The International GNSS Service: Any Questions? (PDF)

Innovation: The International GNSS Service: Any Questions? (PDF)

By Angelyn W. Moore

Published: January 2007 GPS World

In this month’s column, Angelyn Moore, the IGS Central Bureau’s deputy director, overviews the organization’s service, history, and future, demonstrating that the IGS is a model of scientific collaboration of which not just the GNSS community but the whole world should be proud.—Richard Langley

January 1, 2007
Innovation: GNSS Radio (PDF)

Innovation: GNSS Radio: A System Analysis and Algorithm Development Research Tool for PCs (PDF)

By J.K. Ray, S.M. Deshpande, R.A. Nayak, and M.E. Cannon

Published: May 2006 GPS World

In this month’s column, a team of researchers from India and Canada describe a GNSS radio and how they have used it to develop and test algorithms for processing both legacy L1 GPS signals and the new L2C signal.—Richard Langley

May 1, 2006
Innovation: Spacecraft Navigator
Autonomous GPS Positioning at High Earth Orbits

To initially acquire the GPS signals, a receiver also would have to search quickly through the much larger range of possible Doppler shifts and code delays than those experienced by a terrestrial receiver.

By William Bamford, Luke Winternitz and Curtis Hay

INNOVATION INSIGHTS by Richard Langley

GPS RECEIVERS have been used in space to position and navigate satellites and rockets for more than 20 years. They have also been used to supply accurate time to satellite payloads, to determine the attitude of satellites, and to profile the Earth’s atmosphere. And GPS can be used to position groups of satellites flying in formation to provide high-resolution ground images as well as small-scale spatial variations in atmospheric properties and gravity.

Receivers in low Earth orbit have virtually the same view of the GPS satellite constellation as receivers on the ground. But satellites orbiting at geostationary altitudes and higher have a severely limited view of the main beams of the GPS satellites. The main beams are either directed away from these high-altitude satellites or they are blocked to a large extent by the Earth.

Typically, not even four satellites can be seen by a conventional receiver. However, by using the much weaker signals emitted by the GPS satellite antenna side lobes, a receiver may be able track a sufficient number of satellites to position and navigate itself. To initially acquire the GPS signals, a receiver also would have to search quickly through the much larger range of possible Doppler shifts and code delays than those experienced by a terrestrial receiver.

In this month’s column, William Bamford, Luke Winternitz, and Curtis Hay discuss the architecture of a receiver with these needed capabilities — a receiver specially designed to function in high Earth orbit. They also describe a series of tests performed with a GPS signal simulator to validate the performance of the receiver here on the ground — well before it debuts in orbit.

“Innovation” is a regular column featuring discussions about recent advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering at the University of New Brunswick, who appreciates receiving your comments and topic suggestions. To contact him, see the “Columnists” section in this issue.

Calculating a spacecraft’s precise location at high orbits — 22,000 miles (35,400 kilometers) and beyond — is an important and challenging problem. New and exciting opportunities become possible if satellites are able to autonomously determine their own orbits.

First, the repetitive task of periodically collecting range measurements from terrestrial antennas to high-altitude spacecraft becomes less important — this lessens competition for control facilities and saves money by reducing operational costs. Also, autonomous navigation at high orbital altitudes introduces the possibility of autonomous station-keeping. For example, if a geostationary satellite begins to drift outside of its designated slot, it can make orbit adjustments without requiring commands from the ground. Finally, precise onboard orbit determination opens the door to satellites flying in formation — an emerging concept for many scientific space applications.

Realizing these benefits is not a trivial task. While the navigation signals broadcast by GPS satellites are well suited for orbit and attitude determination at lower altitudes, acquiring and using these signals at geostationary (GEO) and highly elliptical orbits (HEOs) is much more difficult. This situation is illustrated in FIGURE 1.

Figure 1. GPS signal reception at GEO and HEO orbital altitudes.

The light blue trace shows the GPS orbit at approximately 12,550 miles (20,200 kilometers) altitude. GPS satellites were designed to provide navigation signals to terrestrial users — because of this, the antenna array points directly toward the Earth. GEO and HEO orbits, however, are well above the operational GPS constellation, making signal reception at these altitudes more challenging. The nominal beamwidth of a Block II/IIA GPS satellite antenna array is approximately 42.6 degrees. At GEO and HEO altitudes, the Earth blocks most of these primary beam transmissions, leaving only a narrow region of nominal signal visibility near the limb of the Earth.This region is highlighted in gray.

If GPS receivers at GEO and HEO orbits were designed to use these higher power signals only, precise orbit determination would not be practical. Fortunately, the GPS satellite antenna array also produces side-lobe signals at much lower power levels. The National Aeronautics and Space Administration (NASA) has designed and tested the Navigator, a new GPS receiver that can acquire and track these weaker signals, dramatically increasing signal visibility at these altitudes.

While using much weaker signals is a fundamental requirement for a high orbital altitude GPS receiver, it is certainly not the only challenge. Other unique characteristics of this application must also be considered. For example, position dilution of precision (PDOP) figures are much higher at GEO and HEO altitudes because visible GPS satellites are concentrated in a much smaller region with respect to the spacecraft antenna. These poor PDOP values contribute considerable error to the point-position solutions calculated by the spacecraft GPS receiver.

Extreme Conditions. Finally, spacecraft GPS receivers must be designed to withstand a variety of extreme environmental conditions. Variations in acceleration between launch and booster separation are extreme. Temperature gradients in the space environment are also severe. Furthermore, radiation effects are a major concern — spaceborne GPS receivers should be designed with radiation-hardened parts to minimize damage caused by continuous exposure to low-energy radiation as well as damage and operational upsets from high-energy particles. Perhaps most importantly, we typically cannot repair or modify a spaceborne GPS receiver after launch. Great care must be taken to ensure all performance characteristics are analyzed before liftoff.

Motivation

As mentioned earlier, for a GPS receiver to autonomously navigate at altitudes above the GPS constellation, its acquisition algorithm must be sensitive enough to pick up signals far below that of the standard space receiver. This concept is illustrated in FIGURE 2. The colored traces represent individual GPS satellite signals. The topmost dotted line represents the typical threshold of traditional receivers. It is evident that such a receiver would only be able to track a couple of the strong, main-lobe signals at any given time, and would have outages that can span several hours.

The lower dashed line represents the design sensitivity of the Navigator receiver. The 10 dB reduction allows Navigator to acquire and track the much weaker side-lobe signals. These side lobes augment the main lobes when available, and almost completely eliminate any GPS signal outages. This improved sensitivity is made possible by the specialized acquisition engine built into Navigator’s hardware.

Figure 2. Simulated received power at GEO orbital altitude.

Acquisition Engine

Signal acquisition is the first, and possibly most difficult, step in the GPS signal processing procedure. The acquisition task requires a search across a three-dimensional parameter space that spans the unknown time delay, Doppler shift, and the GPS satellite pseudorandom noise codes. In space applications, this search space can be extremely large, unless knowledge of the receiver’s position, velocity, current time, and the location of the desired GPS satellite are available beforehand.

Serial Search. The standard approach to this problem is to partition the unknown Doppler-delay space into a sufficiently fine grid and perform a brute force search over all possible grid points. Traditional receivers use a handful of tracking correlators to serially perform this search. Without sufficient information up front, this process can take 10–20 minutes in a low Earth orbit (LEO), or even terrestrial applications, and much longer in high-altitude space applications. This delay is due to the exceptionally large search space the receiver must hunt through and the inefficiency of serial search techniques.

Acquisition speed is relevant to the weak signal GPS problem, because acquiring weak signals requires the processing of long data records. As it turns out, using serial search methods (without prior knowledge) for weak signal acquisition results in prohibitively long acquisition times.

Many newer receivers have added specialized fast-acquisition capability. Some employ a large array of parallel correlators; others use a 32- to 128-point fast Fourier transform (FFT) method to efficiently resolve the frequency dimension. These methods can significantly reduce acquisition time. Another use of the FFT in GPS acquisition can be seen in FFT-correlator-based block-processing methods, which offer dramatically increased acquisition performance by searching the entire time-delay dimension at once. These methods are popular in software receivers, but because of their complexity, are not generally used in hardware receivers.

Exceptional Navigator. One exception is the Navigator receiver. It uses a highly specialized hardware acquisition engine designed around an FFT correlator. This engine can be thought of as more than 300,000 correlators working in parallel to search the entire Doppler-delay space for any given satellite. The module operates in two distinct modes: strong signal mode and weak signal mode. Strong signal mode processes a 1 millisecond data record and can acquire all signals above –160 dBW in just a few seconds. Weak signal mode has the ability to process arbitrarily long data records to acquire signals down to and below –175 dBW. At this level, 0.3 seconds of data are sufficient to reliably acquire a signal.

Additionally, because the strong, main-lobe, signals do not require the same sensitivity as the side-lobe signals, Navigator can vary the length of the data records, adjusting its sensitivity on the fly. Using essentially standard phase-lock-loop/delay-lock-loop tracking methods, Navigator is able to track signals down to approximately –175 dBW. When this tracking loop is combined with the acquisition engine, the result is the desired 10 dB sensitivity improvement over traditional receivers. FIGURE 3 illustrates Navigator’s acquisition engine.

Powered by this design, Navigator is able to rapidly acquire all GPS satellites in view, even with no prior information. In low Earth orbit, Navigator typically acquires all in-view satellites within one second, and has a position solution as soon as it has finished decoding the ephemeris from the incoming signal. In a GEO orbit, acquisition time is still typically under a minute.

Figure 3. Navigator signal acquisition engine.

Navigator breadboard.

GPS constellation simulator.

Navigator Hardware

Outside this unique acquisition module, Navigator employs the traditional receiver architecture: a bank of hardware tracking correlators attached to an embedded microprocessor. Navigator’s GPS signal-processing hardware, including both the tracking correlators and the acquisition module, is implemented in radiation-hardened field programmable gate arrays (FPGAs). The use of FPGAs, rather than an application-specific integrated circuit, allows for rapid customization for the unique requirements of upcoming missions. For example, when the L2 civil signal is implemented in Navigator, it will only require an FPGA code change, not a board redesign.

The current Navigator breadboard—which, during operation, is mounted to a NASA-developed CPU card—is shown in the accompanying photo. The flight version employs a single card design and, as of the writing of this article, is in the board-layout phase. Flight-ready cards will be delivered in October 2006.

Integrated Navigation Filter

Even with its acquisition engine and increased sensitivity, Navigator isn’t always able to acquire the four satellites needed for a point solution at GEO altitudes and above. To overcome this, the GPS Enhanced Onboard Navigation System (GEONS) has been integrated into the receiver software. GEONS is a powerful extended Kalman filter with a small package size, ideal for flight-software integration. This filter makes use of its internal orbital dynamics model in conjunction with incoming measurements to generate a smooth solution, even if fewer than four GPS satellites are in view.

The GEONS filter combines its high-fidelity orbital dynamics model with the incoming measurements to produce a smoother solution than the standard GPS point solution. Also, GEONS is able to generate state estimates with any number of visible satellites, and can provide state estimation even during complete GPS coverage outages.

Hardware Test Setup

We used an external, high-fidelity orbit propagator to generate a two-day GEO trajectory, which we then used as input for the Spirent STR4760 GPS simulator. This equipment, shown in the accompanying photo, combines the receiver’s true state with its current knowledge of the simulated GPS constellation to generate the appropriate radio frequency (RF) signals as they would appear to the receiver’s antenna. Since there is no physical antenna, the Spirent SimGEN software package provides the capability to model one.

The Navigator receiver begins from a cold start, with no advance knowledge of its position, the position of the GPS satellites, or the current time. Despite this lack of information, Navigator typically acquires its first satellites within a minute, and often has its first position solution within a few minutes, depending on the number of GPS satellites in view. Once a position solution has been generated, the receiver initializes the GEONS navigation filter and provides it with measurements on a regular, user-defined basis. The Navigator point solution is output through a high-speed data acquisition card, and the GEONS state estimates, covariance, and measurement residuals are exported through a serial connection for use in data analysis and post-processing.

We configured the GPS simulator to model the receiving antenna as a hemispherical antenna with a 135-degree field-of-view and 4 dB of received gain, though this antenna would not be optimal for the GEO case. Assuming a nadir-pointing antenna, all GPS signals are received within a 40-degree angle with respect to the bore sight. Furthermore, no signals arrive from between 0 and 23 degrees elevation angle because the Earth obstructs this range. An optimal GEO antenna (possibly a high-gain array) would push all of the gain into the feasible elevation angles for signal reception, which would greatly improve signal visibility for Navigator (a traditional receiver would still not see the side lobes). Nonetheless, the following results provide an important baseline and demonstrate that a high-gain antenna, which would increase size and cost of the receiver, may not be necessary with Navigator. The GPS satellite transmitter gain patterns were set to model the Block II/IIA L1 reference gain pattern.

Simulation Results

To validate the receiver designs, we ran several tests using the configuration described above. The following section describes the results from a subset of these tests.

Tracked Satellites. The top plot of FIGURE 4 illustrates the total number of satellites tracked by the Navigator receiver during a two-day run with the hemispherical antenna. On average, Navigator tracked between three and four satellites over the simulation period, but at times as many as six and as few as zero were tracked. The middle pane depicts the number of weak signals tracked—signals with received carrier-to-noise-density ratio of 30 dB-Hz or less. The bottom panel shows how many satellites a typical space receiver would pick up. It is evident that Navigator can track two to three times as many satellites at GEO as a typical receiver, but that most of these signals are weak.

Figure 4. Number of satellites tracked in GEO simulation.

Acquisition Thresholds. The received power of the signals tracked with the hemispherical antenna is plotted in the top half of FIGURE 5. The lowest power level recorded was approximately –178 dBW, 3 dBW below the design goal. (Note the difference in scale from Figure 1, which assumed an additional 6 dB of antenna gain.) The bottom half of Figure 5 shows a histogram of the tracked signals. It is clear that most of the signals tracked by Navigator had received power levels around –175 dBW, or 10 dBW weaker than a traditional receiver’s acquisition threshold.

Figure 5. Signal tracking data from GEO simulation.

Navigation Filter. To validate the integration of the GEONS software, we compared its estimated states to the true states over the two-day period. These results are plotted in FIGURE 6. For this simulation, we assumed that GPS satellite clock and ephemeris errors could be corrected by applying NASA’s Global Differential GPS System corrections, and errors caused by the ionosphere could be removed by masking signals that passed close to the Earth’s limb. The truth environment consisted of a 70X70 degree-and-order gravity model and sun-and-moon gravitational effects, as well as drag and solar-radiation pressure forces. GEONS internally modeled a 10X10 gravity field, solar and lunar gravitational forces, and estimated corrections to drag and solar-radiation pressure parameters. (Note that drag is not a significant error source at these altitudes.) Though the receiver produces pseudorange, carrier-phase, and Doppler measurements, only the pseudorange measurement is being processed in GEONS.

Figure 6. GEONS state estimation errors for GEO simulation.

The results, compiled in TABLE 1, show that the 3D root mean square (r.m.s.) of the position error was less than 10 meters after the filter converges. The velocity estimation agreed very well with the truth, exhibiting less than 1 millimeter per second of three-dimensional error. Navigator can provide excellent GPS navigation data at low Earth orbit as well, with the added benefit of near instantaneous cold-start signal acquisition. For completeness, the low Earth orbit results are included in Table 1.

Navigator’s Future

Navigator’s unique features have attracted the attention of several NASA projects. In 2007, Navigator is scheduled to launch onboard the Space Shuttle as part of the Hubble Space Telescope Servicing Mission 4: Relative Navigation Sensor (RNS) experiment. Additionally, the Navigator/GEONS technology is being considered as a critical navigational instrument on the new Geostationary Operational Environmental Satellites (GOES-R).

In another project, the Navigator receiver is being mated with the Intersatellite Ranging and Alarm System (IRAS) as a candidate absolute/relative state sensor for the Magnetospheric Multi-Scale Mission (MMS). This mission will transition between several high-altitude highly elliptical orbits that stretch well beyond GEO. Initial investigations and simulations using the Spirent simulator have shown that Navigator/GEONS can easily meet the mission’s positioning requirements, where other receivers would certainly fail.

Conclusion

NASA’s Goddard Space Flight Center has conducted extensive test and evaluation of the Navigator GPS receiver and GEONS orbit determination filter. Test results, including data from RF signal simulation, indicate the receiver has been designed properly to autonomously calculate precise orbital information at altitudes of GEO and beyond. This is a remarkable accomplishment, given the weak GPS satellite signals observed at these altitudes. The GEONS filter is able to use the measurements provided by the Navigator receiver to calculate precise orbits to within 10 meters 3D r.m.s. Actual flight test data from future missions including the Space Shuttle RNS experiment will provide further performance characteristics of this equipment, from which its suitability for higher orbit missions such as GOES-R and MMS can be confirmed.

Manufacturers

The Navigator receiver was designed by the NASA Goddard Space Flight Center Components and Hardware Systems Branch (Code 596) with support from various contractors. The 12-channel STR4760 RF GPS signal simulator was manufactured by Spirent Communications (www.spirentcom.com).

FURTHER READING
- 1. Navigator GPS receiver
“Navigator GPS Receiver for Fast Acquisition and Weak Signal Tracking Space Applications” by L. Winternitz, M. Moreau, G. Boegner, and S. Sirotzky, in Proceedings of ION GNSS 2004, the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation, Long Beach, California, September 21–24, 2004, pp. 1013-1026.

“Real-Time Geostationary Orbit Determination Using the Navigator GPS Receiver” by W. Bamford, L. Winternitz, and M. Moreau in Proceedings of NASA 2005 Flight Mechanics Symposium, Greenbelt, Maryland, October 18–20, 2005 (in press). A pre-publication version of the paper is available online at http://www.emergentspace.com/pubs/Final_GEO_copy.pdf.
- 1. GPS on high-altitude spacecraft
“The View from Above: GPS on High Altitude Spacecraft” by T.D. Powell in GPS World, Vol. 10, No. 10, October 1999, pp. 54–64.

“Autonomous Navigation Improvements for High-Earth Orbiters Using GPS” by A. Long, D. Kelbel, T. Lee, J. Garrison, and J.R. Carpenter, paper no. MS00/13 in Proceedings of the 15th International Symposium on Spaceflight Dynamics, Toulouse, June 26–30, 2000. Available online at http://geons.gsfc.nasa.giv/library_docs/ISSFDHEO2.pdf.
- 1. GPS for spacecraft formation flying
“Autonomous Relative Navigation for Formation-Flying Satellites Using GPS” by C. Gramling, J.R. Carpenter, A. Long, D. Kelbel, and T. Lee, paper MS00/18 in Proceedings of the 15th International Symposium on Spaceflight Dynamics, Toulouse, June 26–30, 2000. Available online at http://geons.gsfc.nasa.giv/library_docs/ISSFDrelnavfinal.pdf.

“Formation Flight in Space: Distributed Spacecraft Systems Develop New GPS Capabilities” by J. Leitner, F. Bauer, D. Folta, M. Moreau, R. Carpenter, and J. How in GPS World, Vol. 13, No. 2, February 2002, pp. 22–31.
- 1. Fourier transform techniques in GPS receiver design
“Block Acquisition of Weak GPS Signals in a Software Receiver” by M.L. Psiaki in Proceedings of ION GPS 2001, the 14th International Technical Meeting of the Satellite Division of The Institute of Navigation, Salt Lake City, Utah, September 11–14, 2001, pp. 2838–2850.
- 1. Testing GPS receivers before flight
“Pre-Flight Testing of Spaceborne GPS Receivers Using a GPS Constellation Simulator” by S. Kizhner, E. Davis, and R. Alonso in Proceedings of ION GPS-99, the 12th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 14–17, 1999, pp. 2313–2323.

BILL BAMFORD is an aerospace engineer for Emergent Space Technology, Inc., in Greenbelt, Maryland. He earned a Ph.D. from the University of Texas at Austin in 2004, where he worked on precise formation flying using GPS as the primary navigation sensor. As an Emergent employee, he has worked on the development of the Navigator receiver and helped support and advance the NASA Goddard Space Flight Center’s Formation Flying Testbed. He can be reached at [email protected].

LUKE WINTERNITZ is an electrical engineer in hardware components and systems at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. He has worked at Goddard for three years primarily in the development of GPS receiver technology. He received bachelor’s degrees in electrical engineering and mathematics from the University of Maryland, College Park, in 2001 and is a part-time graduate student there pursuing a Ph.D. He can be reached at [email protected].

CURTIS HAY served as an officer in the United States Air Force for eight years in a variety of GPS-related assignments. He conducted antijam GPS R&D for precision weapons and managed the GPS Accuracy Improvement Initiative for the control segment. After separating from active duty, he served as the lead GPS systems engineer for OnStar. He is now a systems engineer for Spirent Federal Systems in Yorba Linda, California, a supplier of high-performance GPS test equipment. He can be reached at [email protected].
April 1, 2006