As today’s handsets and consumer devices become more sophisticated, manufacturers continue to incorporate more and more functionality into a small and sleek form factor. Today’s range of smartphones incorporate voice and data transceivers, GPS, Bluetooth, Wi-Fi, cameras, music, touchscreen interfaces, compasses, motion sensors, cameras, storage cards, and many other technologies. Free turn-by-turn navigation services, such as offered on Google Android phones and iPhones, have created a compelling reason for many of us to own a GPS-equipped smartphone.
The pressure on manufacturers to integrate so many functions into one small printed circuit board has fueled a race among semiconductor suppliers to offer new solutions combining GPS and wireless connectivity. Phones that are small and comfortable to hold mean less and less space available for the internal electronics. Large screen sizes and the trend to thinner and thinner devices means smaller, less efficient antennas, placing pressure on chip designers to improve integrated circuit (IC) performance to make up for antenna constraints.
Finally, cost competition in these markets is intense, as operators compete to bring more users online.
These forces have shaped several changes in the wireless semiconductors found in new smartphones. Three important enabling technologies are:
reduced-geometry semiconductor technologies,
wafer-scale packaging, and
combo chip integration.
Let’s look at the trends in each area.
Semiconductor transistor sizes have been shrinking for decades. GPS processors in the market today use transistor geometries with gate widths of 0.18 micrometers, 0.13 micrometers, 90 nanometers (nm), and 65 nm, the latter showing up in the newest handsets on the market. 40-nm-based ICs have been announced as well, and will find their way into the market in the next year or two.
Each generation of technology offers a 50–100 percent increase in density for pure digital circuits. This so-called shrink has allowed designers to both reduce the size of chips and to pack in more performance — in GPS chips this usually means more tracking channels and more correlators for faster signal search. The area for non-digital circuits such as the radio receiver in a GPS has not been shrinking as fast as the digital portion. This had led to changes in architecture, with more and more functions going digital. Examples include digital band-shaping filters, digital gain adjustment, and sigma-delta analog- to-digital converters.
Wafer-scale packaging has moved into the mainstream for GPS and other wireless ICs. Traditional ball-grid array (BGA) packaging requires placing a semiconductor die on a substrate. The substrate carries the balls (pins) and some interconnects, and the semiconductor die is connected to the substrate via wire bonds. For small ICs the overall package size may be 50 percent larger than the die itself, because of overhead of the space needed for wire bonds.
By contrast, wafer-level ball grid array (WLBGA) packaging yields a finished packaged part with the same dimensions as the underlying die. Wire bonds are not used; a redistribution layer (RDL) is bonded to the silicon wafers and carries interconnections from the silicon to the balls. This type of packaging yields the smallest possible board footprint. It also places strict limitations on the number of package pins, since the pins must all fit under the chip and cannot be spaced too closely, due to board manufacturing constraints. Often designers struggle to provide the features customers seek while abiding by package pin-count limitations. Pins are shared or multiplexed to preserve flexibility.
Combo-chip integration offers the ultimate solution for small size. A single IC with multiple functions will almost always be considerably smaller than several ICs on a printed board. The last two years have seen the introduction of several combo ICs containing GPS, including the Broadcom’s BCM2075 Bluetooth-FM-GPS combo IC. Combo ICs like this allow manufacturers to build cellular handsets that would be difficult or impossible to create using discrete chip sets. Since GPS, FM, and Bluetooth have become standard features across many product lines, manufacturers not only benefit from small size but also economies of scale, designing a single part into dozens of devices.
The benefits of combo ICs are easy to understand, but making these devices brings unique challenges. First and foremost, these ICs are wireless devices containing multiple sensitive radios, where every fraction of a decibel of performance counts. With few exceptions, handset manufacturers and their wireless operator customers are not willing to sacrifice radio performance in their quest for miniaturization and cost reduction. Each function on the wireless combo IC must perform as well as its counterpart function in a stand-alone IC.
However, in a combo IC the radios are at most a few millimeters apart from each other. Designing for this type of integration requires engineering attention at multiple stages of the design. Up front, during the system engineering phase, component specifications must be set that minimize interference between radio subsystems, considering not just the radios on the combo IC but the influence of other radios in a handset as well. For example, in setting the specification for the second-order intercept point of the GPS receiver, system engineers must consider the fact that transmissions in 825 MHz cellular band can mix with Bluetooth transmissions at 2400 MHz to yield an intermodulation product at 1575 MHz, right in the middle of the GPS receive band. Designers also choose clock frequencies to avoid interference; for example, a GPS baseband processor that clocks at 100 MHz might be changed to 75 MHz to avoid the FM receive band. These are just a couple of examples of the many scenarios and considerations that must be examined early in the design process.
Once the system engineer has done his or her job, the next level of interference mitigation falls on the analog designers. They choose where to place circuits, how to structure the semiconductor layers, how to drive and load interconnects, and how to properly filter supply voltages to avoid undesired interactions. Keeping spurious products off local oscillator signals is a key challenge. GPS receivers have 100 dB or more of gain to amplify very weak GPS signals to a usable level. Due to this high gain, even a tiny spurious product on a local oscillator can have the effect of tuning in an undesired cellular transmitter. For example, a spurious product offset 135 MHz will tune a cellular transmitter at 1710 MHz down to 1575 MHz, again right in the middle of the GPS band. Avoiding these interactions requires experienced designers who can anticipate complex issues. Mistakes can be costly, with each mask for each IC iteration going into seven figures.
As the challenges of combo ICs are overcome, it’s likely the future will bring even more in the way of wireless technology integration. This in turn will provide even more opportunities for GPS to penetrate a broader set of handsets and cellular devices, making this exciting technology available to more consumers every day.
CHARLES ABRAHAM is senior director of engineering for the GPS Business Unit at Broadcom, which he joined via acquisition of Global Locate, a company he co-founded in 2000. Previously, he worked at Ashtech, Magellan, Trimble, and Hughes Electronics.
By John Nielsen, Ali Broumandan, and Gérard Lachapelle
Ubiquitous adoption of and reliance upon GPS makes national and commercial infrastructures increasingly vulnerable to attack by criminals, terrorists, or hackers. Some GNSS signals such as GPS P(Y) and M-code, GLONASS P-code, and Galileo’s Public Regulated Service have been encrypted to deny unauthorized access; however, the security threat of corruption of civilian GNSS signals increases constantly and remains an unsolved problem. We present here an efficient approach for the detection and mitigation of spoofed GNSS signals, as a proposed countermeasure to add to the existing system.
Current methods to protect GPS civilian receivers from spoofing signals are based on the cross-check with available internal/external information such as predictable characteristics of the navigation data bits or correlation with ancillary inertial-based sensors; alternately, a joint process of signals received at two separate locations based on processing the P(Y)-code.
The authentic GNSS signal sourced from a satellite space vehicle (SV) is very weak at the receiver’s location and is therefore vulnerable to hostile jamming based on narrowband noise radiation at a modest power level. As the GNSS frequency band is known to the jammer, the effectiveness of the latter is easily optimized by confining radiation to within the GNSS signal band. The jammed GNSS receiver is denied position or time estimates which can be critical to the mission. While noise jamming of the GNSS receiver is a threat, the user is easily aware of its existence and characteristics. The worst case is that GNSS-based navigation is denied.
A more significant jamming threat currently emerging is that of the spoofing jammer where bogus signals are transmitted from the jammer that emulate authentic GNSS signals. This is done with multiple SV signals in a coordinated fashion to synthesize a plausible navigation solution to the GNSS receiver. There are several means of detecting such spoofing jammers, such as amplitude discrimination, time-of-arrival discrimination, consistency of navigation inertial measurement unit (IMU) cross-check, polarization discrimination, angle-of-arrival (AOA) discrimination, and cryptographic authentication.
Among these authentication approaches, the AOA discriminator and spatial processing have been addressed and utilized widely to recognize and mitigate hostile attacks. We focus here on the antenna-array processing problem in the context of spoofing detection, with considerations to the pros and cons of the AOA discriminator for handheld GNSS receivers.
An exploitable weakness of the spoofing jammer is that for practical deployment reasons, the spoofing signals generally come from a common transmitter source. Hence, a single jamming antenna sources the spoofing signals simultaneously. This results in a means of possible discrimination between the real and bogus GNSS signals, as the authentic GNSS signals will emanate from known bearings distributed across the hemisphere.
Furthermore, the bearing of the jammer as seen from the GNSS receiver will be different than the bearing to any of the tracked GNSS satellites or space vehicles (SV). This immediately sets up some opportunities for the receiver to reject the spoofing jamming signals. Processing can be built into the receiver that estimates the bearing of each SV signal. Note that the relative bearings of the GNSS signals are sufficient in this case, as the bogus signals will all have a common bearing while the authentic GNSS signals will always be at different bearings.
If the receiver comprises multiple antennas that have an unobstructed line of sight (LOS) to the SVs, then there are possibilities of spoofing detection based on the common bearing of the received GNSS signals and eliminating all the jammer signals simultaneously by appropriate combining of the receiver antennas to form a pattern null coincident with the jammer bearing.
Unfortunately, the AOA discrimination will not be an option if the jammer signal or authentic signals are subjected to spatial multipath fading. In this case, the jammer and individual SV signals will come in from several random bearings simultaneously. Furthermore, if the GNSS receiver is constrained by the form factor of a small handset device, an antenna array will not be an option. As the carrier wavelength of GNSS signals is on the order of 20 to 25 centimeters, at most two antennas can be considered for the handset receiver, which can be viewed as an interferometer with some ability of relative signal-bearing estimation as well as nulling at specific bearings.
However, such an antenna pair is not well represented by independent isotropic field sampling nodes, but will be significantly coupled and strongly influenced by the arbitrary orientation that the user imposes. Hence, the handset antenna is poorly suited for discrimination of the spoofing signal based on bearing. Furthermore, handheld receivers are typically used in areas of multipath or foliage attenuation, and therefore the SV signal bearing is random with significant variations.
As we discuss here, effective spoofing detection is still possible for a single antenna GNSS receiver based on the differing spatial correlation of the spoofing and authentic signals in the proximity of the receiver antenna. The basic assumption is that the antenna will be spatially moved while collecting GNSS signal snapshots. Hence, the moving antenna generates a signal snapshot output similar to that of a synthetic array (SA), which, under some additional constraints, can provide an effective means of detecting the source of the GNSS signals from a spoofing jammer or from an authentic set of SVs.
We assume here an arbitrary antenna trajectory with the spoofing and authentic signals subjected to random spatial multipath fading. The processing will be based on exploiting the difference in the spatial correlation of the spoofing and the authentic signals.
Spoofing Detection Principle
Consider a GNSS handset receiver (Figure 1) consisting of a single antenna that is spatially translated in time along an arbitrary trajectory as the signal is processed by the GNSS receiver. There are L authentic GNSS SV signals visible to the receiver, along with a jammer source that transmits spoofing replicas of the same Lauthentic signals.
FIGURE 1. GNSS receiver with a single antenna and 2L parallel despreading channels simultaneously providing channel gain estimates of L authentic and L spoofing signals as the antenna is moved along an arbitrary spatial trajectory.
It is assumed that the number of spoofed signals range from 1 to L, which are coordinated such that they correspond to a realistic navigation solution at the output of the receiver processing. The code delay and Doppler associated with the spoofing signals will typically be different than those of the authentic signal. The basic technique of coordinated spoofing jamming is to present the receiver with a set of L signals that appear to be sufficiently authentic such that the spoofing and authentic signal sets are indistinguishable. Then the spoofing signals separate slowly in terms of code delay and Doppler such that the navigation solution corresponding to the L spoofing signals will pull away from the authentic navigation solution.
The focus herein is on methods where the authenticity of the L tracked GNSS signals can be tested directly by the standalone receiver and then selected for the navigation processing. This is in contrast with other methods where the received signals are transmitted back to a communication command center for verification of authenticity. The consideration here is on the binary detection problem of assessing if each of the 2L potential signals is authenti
c or generated by a spoofing source. This decision is based on observations of the potential 2L GNSS signals as the antenna is spatially moved through the trajectory.
The complex baseband signal at the output of the antenna, denoted by r(t), can be expressed as
where i is the GNSS signal index, the superscripts A and J indicate authentic and jamming signals respectively, p(t) shows the physical position vector of the moving antenna phase center relative to a stationary spatial coordinate system, ΛAi(p(t),t) and ΛJi(p(t),t) give the channel gain for the authentic and the spoofing signals of the ith SV at time t and position p, ci(t) is the PN coding modulation of ith GNSS signal, πAi and πJi are the code delay of ith PN sequence corresponding to the authentic and the spoofing sources respectively, fDiA and fDiJ are the Doppler frequency of the ith authentic and the spoofing signals and w(t) represents the complex baseband of additive noise of receiver antenna. For convenience, it is assumed that the signal index iε[1, 2,…,L] is the same for the spoofing and authentic GNSS signals. The spoofer being aware of which signals are potentially visible to the receiver will transmit up to L different spoofing signals out of this set.
Another simplification that is implied by Equation 1 is that the message coding has been ignored, which is justifiable as the GNSS signals are being tracked such that the message symbol modulation can be assumed to be removable by the receiver by some ancillary process that is not of interest in the present context. The objective of the receiver despreading operation is to isolate the channel gains ΛA(p(t),t) ΛJ(p(t),t), which are raw observables used in the subsequent detection algorithm.
It is assumed that the GNSS receiver is in a signal tracking state. Hence, it is assumed that the data coding, code phase of the spreading signal and Doppler are known inputs in the despreading operation. The two outcomes of the ith despreading channel for authentic and jamming signals are denoted as riA(t) and rkJ(t) respectively, as shown in Figure 1. This notation is used for convenience and not to imply that the receiver has knowledge of which of the pair of GNSS signals corresponds to the authentic or spoofer cases. The receiver processing will test each signal for authenticity to select the set of L signals that are passed to the navigation estimator.
The despread signals riA(t) and rkJ(t) are collected over a snapshot interval of tε[0,T]. As the notation is simplified if discrete samples are considered, this interval is divided into M subintervals each of duration ΔT such that the mth subinterval extends over the interval of [(m−1)ΔT,mΔT]for mε[1,,2,…,M]. The collection of signal over the first and mth subintervals is illustrated in Figure 2. ΔT is considered to be sufficiently small such that ΛAi(p(t),t) or ΛJk(p(t),t) is approximately constant over this interval leading a set of M discrete samples for each despreading output. From this the vectors form of channel gain sample and outputs of despreaders can be defined by
where ΛAi(p(mΔT),mΔT) and ΛJi(p(mΔT),mΔT) are the mth time sample of the ith despreader channel for the authentic and jamming GNSS signals.
Figure 2. Spatial sampling of the antenna trajectory into M subinterval segments.
Pairwise Correlation
The central tenet of the spoofing detection is that the array gain vector denoted here as the array manifold vector for the jammer signals ΛJ will be the same for all of the L spoofing signals while the array manifold vector for the authentic signals ΛA will be different for each of the L authentic signals. If the random antenna trajectory is of sufficient length, then the authentic signal array manifold vectors will be uncorrelated. On the other hand, as the jammer signals emerge from the same source they will all have the same array manifold vector regardless of the random antenna trajectory and also regardless of the spatial fading condition. This would indicate that a method of detecting that a spoofer is present to form the Mx2L matrix of all of the despreader output vectors denoted as r and given as
where it is assumed that M≥2L.
Basically what can be assumed is that, if there is a spoofer from a common source that transmits more than one GNSS signal simultaneously, there will be some residual spatial correlation of the observables of ΛJi with other despreader outputs of the receiver. Therefore, if operations of pairwise correlations of all of the 2L despreader outputs result in high correlation, there is a likelihood of the existence of spoofing signals. These pairwise correlations can also be used to distinguish spoofing from authentic signals. Note that even during the time when the spoofing and authentic signals have the same Doppler and code offset, the superposition manifold vector of ΛAi and ΛJi will be correlated with other spoofing manifold vectors. The pairwise correlation of the various spoofing signals can be quantified based on the standard numerical estimate of the correlation coefficient given as
where ri is the ith column vector of r defined in Equation 3, and the superscript H denotes the complex conjugate operator.
Toward Spoofing Detection
Figure 3 shows the spoofing attack detection and mitigation methodology:
The receiver starts with the acquisition process of a given GNSS code. If, for each PN sequence, there is more than one strong peak above the acquisition threshold, the system goes to an alert state and declares a potential spoofing attack. Then the receiver starts parallel tracking on each individual signal.
The outputs of the tracking pass to the discriminator to measure the correlation coefficient ρ among different PN sequences. As shown in Figure 3, if ρ is greater than a predefined threshold ϒ, the receiver goes to defensive mode. As the spoofer attempts to pull the tracking point off the authentic signals, the spoofer and authentic signals for a period of time will have approximately the same code offset and Doppler frequency. Hence, it may not be possib
le to detect more than one peak in the acquisition mode. However, after a while the spoofer tries to pull tracking mode off.
The outputs of the parallel tracking can be divided into two groups: the J group is the data set that is highly correlated, and the A group is the set that is uncorrelated. It is necessary that the receiver antenna trajectory be of sufficient length (a few tens of the carrier wavelengths) such that M is moderately large to provide a reasonable estimate of the pairwise correlation.
The A group will be constrained in size based on the number of observable satellites. Usually this is known, and L can be set. The receiver has control over this by setting the bank of despreaders. If an SV signal is known to be unobtainable due to its position in the sky, it is eliminated by the receiver. Hence the A group can be assumed to be constrained in size to L. There is the possibility that a spoofer will generate a signal that is clear, while the SV signal is obscured by shadowing obstacles. Hence a spoofing signal can inadvertently be placed in the A group. However, as this signal will be correlated with other signals in the J group, it can be transferred from the A to the J group.
When the spoofing navigation solution pulls sufficiently away from the authentic solution, then the navigation solution can create two solutions, one corresponding to the authentic signals and the other corresponding to the spoofing signals. At this stage, the despreading code delay and Doppler will change such that the authentic and spoofing signals (corresponding to the same GNSS signal) will appear to be orthogonal to each other.
Proper placement of the members in the J and A groups can be reassessed as the set of members in the A group should provide the minimum navigation solution variance. Hence, in general there will be a spoofing and authentic signal that corresponds to the GNSS signal of index i. If the spoofing signal in group J appears to have marginal correlation with its peer in group A and, when interchanged with its corresponding signal in group A, the latter generates a lower solution variance, then the exchange is confirmed.
Figure 3. Spoofing detection and mitigation methodology.
Experimental Measurements
We used two data collection scenarios in experiments of spoofing detection, based on utilizing a single antenna that is spatially translated, to demonstrate the practicality of spoofing-signal detection based on spatial signal correlation discrimination. In the first scenario, the spoofing measurements were conducted inside a modern three-story commercial building. The spoofing signals were generated by a hardware simulator (HWS) and radiated for a few minutes indoors, using a directional antenna pointing downward to affect only a small area of the building. The intention was to generate NLOS propagation conditions with significant multipath.
The second data collection scenario was based on measuring authentic GPS L1 C/A signals under open-sky conditions, in which case the authentic GPS signals are temporally highly correlated. At the particular instance of the spoofing and the authentic GPS signal measurement scenarios, the SVs were distributed as shown in Figure 4. The GPS receiver in both scenarios consisted of an active patch right-hand circular polarized (RHCP) antenna and a down-conversion channelizer receiver that sampled the raw complex baseband signal. The total data record was subsequently processed and consisted in acquiring the correlation peaks based on 20-millisecond coherent integration of the spoofing signals and in extracting the channel gains L as a function of time.
Figure 4. Skyplots of available satellites: a) spoofing signals from Spirent generator, b) authentic signals from rooftop antenna.
Figure 5 shows a plot of the samples of the magnitude of despreader outputs for the various SV signals generated by the spoofing jammer and authentic signals. The signal magnitudes in the spoofing case are obviously highly correlated as expected, since the jammer signals are all emanating from a common antenna. Also, the SNRs are moderately high such that the decorrelation due to the channel noise is not significant.
The pairwise correlation coefficient using Equation 4 are calculated for the measurement results represented in Figure 5 and tabulated in Table 1 and Table 2 for the spoofing and the authentic cases respectively. As evident, and expected, the correlations for the spoofing case are all very high. This is anticipated, as the spoofing signals all occupy the same frequency band with exception of small incidental shifts due to SV Doppler.
Figure 5. Normalized amplitude value of the signal amplitude for different PRNs: a) generated from the same antenna, b) Authentic GPS signals.TABLE 1. Correlation coefficient deter- mined for the set of spoofing signals.TABLE 2. Correlation coefficient deter- mined for the set of authentic signals.
Conclusions
Spoofing signals generated from a common source can be effectively detected using a synthetic array antenna. The key differentiating attribute exploited is that the spoofing signals emanating from a single source are spatially correlated while the authentic signals are not. The method works regardless of the severity of multipath that the spoofing or authentic signals may be subjected to. The receiver antenna trajectory can be random and does not have to be jointly estimated as part of the overall spoofing detection.
A patent is pending on this work.
Manufacturers
The experimental set-up used a Spirent GSS7700 simulator, National Instruments receiver (NI PXI-5600 down converter, and NI PXI-5142 digitizer modules), TECOM directional helical antennas as the transmitter antenna, and NovAtel GPS-701-GG as the receiver antenna.
JOHN NIELSEN is an associate professor at the University of Calgary.
ALI BROUMANDAN is a senior research associate in the Position Location And Navigation (PLAN) group at the University of Calgary. He obtained a Ph.D. in Geomatics Engineering from the University of Calgary in 2009.
GERARD LACHAPELLE holds an iCORE/CRC Chair in Wireless Location and heads the PLAN Group in the Department of Geomatics Engineering at the University of Calgary.
Michibiki has more Twitter followers than you and me put together. All of you, and all of me with my 17 followers. Michibiki hit 16,284 when I signed on just now, and she (he?) has not yet even emerged upon the global stage. Perhaps by the time you read this, if the September 11 launch date holds true, s/he will be an orbiting, broadcasting entity.
Michibiki
Why follow a satellite? One might well ask why follow anything or anyone these days. For utterings momentous or vacuous, leavened in lucky moments by a bit of gossip, or an even rarer bit of news. It’s a good bet that Michibiki’s scriptwriters will display more intelligence than the mass of online mouths. Right now it’s hard to tell; they communicate in Japanese, which comes through my browser as so many question marks.
For intelligence is what the Michibiki anthropomorphizing — from the creation of a friendly, pettable caricature to the establishment of a Twitter voice — is all about. Savvy marketing by purposeful people to an audience that they have studied and know well. This goes beyond the cute that large segments of Japan have a fondness for. It has the goal of buliding a solid, sustained client core for location-based services, powered by QZSS signals.
Other places where LBS have failed to take hold — and this means everywhere — despite their vast potential utility, would do well to watch and learn.
As cell-phone text-message readers and e-mail users (could there be a broader market segment, other than people who eat and breathe?) become accustomed to receiving messages from Michibiki, they will subtly but increasingly think of this 4,000-kilogram, 40,000-kilometer high hunk of orbiting metal and circuitry as a personality, and even, a friend. They will be open to suggestions, impressionable and cute-prone teens and twenty-somethings, especially so. This is the next generation of satnav users. Or I should say, this is the Now Generation of satnav users.
Young men and women with places to go and friends to see will remember Michibiki, and call upon her/him often. “My guide will tell me how to get there. With added services, my guide will also track my friends right now, and tell me where they are. My guide can do many more wonderful things for me: here is a list of them.”
By no means do I suggest that the U.S. GPS Industry Council or the Galileo Supervisory Authority or Roscosmos rush out and commission a cartoon character based on their respective space vehicles. Different markets require different approaches, and careful study.
The Now Generation of satnav users is coming through, to supplant current users, and uses. They’ll soon rattle your windows and shake down your walls. If your time to you is worth saving, I do suggest that you pick up on social media. That is the message my own marketing staff keeps drumming into this obdurate old head.
A receiver can selectively acquire scattered signals and the resulting measurements can be interpreted to reveal certain characteristics of the source of the scattering. This article discusses the design and application of a GNSS instrument that uses scattered signals for monitoring the level and roughness of inland and coastal water surfaces for the betterment of planet Earth.
By Alejandro Egido and Marco Caparrini
INNOVATION INSIGHTS by Richard Langley
WHY IS THE SKY BLUE? This is an age-old question, interesting to anyone with a curiosity about his or her surroundings. But what has it got to do with global navigation satellite systems? Believe it or not, there is a connection.
Some of you might remember the explanation of the sky’s color from your Physics 101 course but to bring everyone up to the same level, let’s review. Everything we see is the result of the interaction of light and matter. And by matter, we mean the atoms, molecules, and particles making up matter. Light causes matter to vibrate. And vibrating matter (due to its electrical charges) in turn emits light, which combines with the original light. But matter not only re-emits light in the forward direction, it re-emits light in all other directions. This is called scattering.
Now, the light from the sun includes all colors and so if look directly at the sun when it is high in the sky (don’t try this at home), it looks white or slightly yellowish. We are seeing the light propagating directly toward our eyes. When we look at the sky away from the sun, we are seeing scattered light. And this scattered light is predominantly blue. Why? It turns out that scattering is proportional to the fourth power of frequency. Light that is of a higher frequency, say a factor of two, is sixteen times more intensely scattered. So, blue light, which has about twice the frequency of light from the red end of the visible spectrum, is scattered much more than red light. Violet light is scattered even more but our eyes are not as sensitive to violet light as they are to blue light. Hence the sky looks blue.
So what has this got to do with GNSS? As we know, for the best positioning and navigation results, we need the satellite signals to travel along a direct path to the receiver’s antenna. There may be slight changes in the speed and direction of propagation of these direct-path signals caused by the interaction of the electromagnetic waves with the matter making up the ionosphere and the neutral atmosphere, but these are readily accounted for in the position fixes.
However, once they reach the Earth’s surface, the signals can be reflected by buildings, vegetation, the ground, water surfaces, and so on. The signals are actually being scattered by the matter they encounter. A receiver can selectively acquire the scattered signals and the resulting measurements can be interpreted to reveal certain characteristics of the source of the scattering.
In this month’s column, we learn about the design and application of a GNSS instrument that uses scattered signals for monitoring the level and roughness of inland and coastal water surfaces–yet one more use of GNSS signals for the betterment of planet Earth.
Lakes and water reservoirs are the world’s most important sources of accessible fresh water. Despite its paramount importance — not only for a large variety of human activities, but also for the sustainability of ecosystems — fresh water is already scarce in many regions. The problem is envisaged to become worse in the coming decade. In addition, in climatological studies surface water storage is a critical element of the water cycle since the analyses integrate all hydrologic processes (precipitation, runoff, evapotranspiration, and so on) over a given basin; and for hydroelectric companies, it is the main parameter to be kept under observation for efficient energy production. All of these concerns make the monitoring of fresh water resources a prime activity for a wide variety of stakeholders including governments, climate research organizations, and hydroelectric production companies.
Coastal management is also a wide-ranging issue with large social and economic impacts. Care of our coasts includes dealing with threats such as storm surges and flooding, coastal erosion, and conflicting land-use issues. Coastal areas support the greatest concentration of living resources and people on the planet. In the past few decades, these regions have experienced a population density increase, which is envisioned to grow steadily. Furthermore, conflicts between commercial interests, recreational activities, infrastructure development, environment conservation, and exploitation of natural resources will become increasingly important and contentious. In fact, the coastal zone is a peculiar environment in which terrestrial, oceanic, atmospheric, and human inputs of energy and matter converge. Storm surges and coastal flooding events have caused considerable damage and economic loss on European coasts in particular. Such events, possibly linked to the world climate change, are expected to get worse in the near future, due to sea level rise and storm activity.
So, close monitoring of both inland waters and coastal regions is necessary for the well being of the planet. And since the need is so pervasive, monitoring systems should be characterized by a relatively low cost, low maintenance, and easy deployment, to serve the widest possible user community. We have developed a patent-pending solution using signals from global navigation satellite systems (GNSS).
Called Oceanpal, our monitoring system exploits reflected GNSS signals as signals of opportunity for passive remote sensing of the Earth’s water surfaces. These multipath signals are usually considered to be nuisance signals since they reduce the accuracy of GNSS positioning applications. But for monitoring various processes affecting the Earth’s surface, they are very beneficial. The technique is known as GNSS reflectometry (GNSS-R), and during the past decade, its use as a technique for Earth observation purposes has taken root.
GNSS-R is basically a bistatic radar technique. While most radar systems, such as those used for monitoring air space and harbor approaches and for weather forecasting, combine the radar transmitter and receiver at the same site — so-called monostatic radar — bistatic systems use transmitters and receivers separated by a considerable distance. Such systems have been used for studying certain atmospheric phenomena and for military applications where simple line-of-sight reflections from the target of interest are inadequate or insufficient.
The concept of bistatic radar can be extended to satellite signals. Since some of the signal transmitted by a satellite gets reflected off the Earth’s surface, detecting this reflected signal by a separate passive receiver would provide some information about the reflecting surface. While any satellite signal could be used in principle, GPS (and other GNSS) turn out to be particularly useful. The concept of using GPS signal reflections was initially proposed in 1993 by Manuel Martín-Neira, working at the European Space Agency’s European Space Research and Technology Centre in Noordwijk, The Netherlands. Since then, the technique has been successfully implemented by an increasing number of researchers.
We could list several reasons for the continuous growing interest in GNSS as a remote sensing tool, but two main ones stand out: first, the global availability and stability of GNSS signals enables their use as reliable signals of opportunity; and second, GNSS makes use of L-band radiation, which is highly interactive with the natural scattering medium but relatively impervious to atmospheric conditions. Moreover, the passive nature of this concept allows for the production of cost- and resource-effective instruments.
Navigation signals are sensitive to a wide variety of geophysical parameters including topography, surface roughness, surface moisture, ionospheric electron content, tropospheric water vapor, water salinity, and vegetation. Research targeting related geophysical applications has been ongoing for many years, and the first pre-operational services exploiting reflected GNSS signals are now available. In fact, while the scientific community is waiting for a dedicated GNSS-R space mission to confirm the theoretical predictions about the characteristics of reflected signals observed from space, ground-based and airborne sensors have already been developed and validated for a number of applications.
The GNSS-R research area that has been most thoroughly investigated concerns the reflection of navigation signals from water surfaces, given the highly reflective nature of water. However, from water the interest has now moved towards ice and land applications, more specifically to the detection of sea ice and the monitoring of soil moisture. Recently, GNSS-R has also been proposed as a possible tool to monitor vegetation. This article focuses on the presentation of the Oceanpal sensor, and the description of the altimetry algorithms for monitoring the levels of sea (coastal) and inland waters.
Our Instrument
As mentioned above, Oceanpal is a GNSS-R-based sensor designed for operational monitoring of coastal and inland waters. The instrument comprises three subsystems: a radio frequency (RF) section, an intermediate frequency (IF) section, and a data-processing section. The RF section features a pair of low gain L-band antennas. A right-hand circularly polarized (RHCP) zenith-facing antenna collects the direct GNSS signals while a left-hand circularly polarized (LHCP) nadir-facing antenna collects the sea- or lake-surface reflected GNSS signals. (On reflection, the signals become predominantly LHCP.) Data bursts of some minutes’ duration are acquired from each antenna using two GPS L1 receivers (front ends) that down-convert the signals to IF. Within the IF sections, the signals are one-bit sampled and stored on a hard disk.
These direct and reflected raw data are then fed into the processing section of the instrument, where a pair of software GNSS receivers detects and tracks the available signals in the direct channel (which works as a master) and blindly despreads the reflected signals in the reflected (slave) channel. The result of this processing is a set of direct and reflected electromagnetic field time series for each satellite in view, plus some ancillary information, such as the satellite pseudorandom noise code (PRN) numbers and GPS time references, among others. The architecture described above is shown in FIGURE 1.
Figure 1. Basic operation of Oceanpal and the principle of GNSS-R-based sea-surface monitoring. Right-hand and left-hand circularly polarized antennas feed signals to radio frequency (RF) receiver front-ends that, in turn, feed software (SW) receiver back-ends and subsequent processing algorithms.
The data products provided by Oceanpal are so-called “Level-2” or derived products, namely the significant wave height (a statistical measure of trough-to-crest wave height), and the height of the nadir antenna over the mean level of the water surface under observation. To make this data available for the user in a friendly way, the observations are uploaded to a web server and displayed on a web page.
Oceanpal requires low maintenance compared to its competitors. Standard oceanographic buoys, which use accelerometers and a magnetic compass, or GPS buoys, featuring a conventional GPS receiver, are in contact with water, which implies costly infrastructures and frequent maintenance operations. Pressure sensors and air bubblers, commonly used to monitor the level of water reservoirs, also require frequent maintenance because of sediment accumulation. Compared to the alternatives, our sensor is a less costly and lower maintenance solution.
GNSS-R Altimetry Algorithms
The inland-water/sea-level monitoring is based on the estimation of the height of the Oceanpal antennas above the water/sea surface. This height is retrieved by the comparison of the delay (in time or distance) between the reflected and the direct signals. The reflection geometry is shown in FIGURE 2. Such a delay can be estimated using either the PRN code or the carrier phase of the incoming signals. The phase-based estimation provides more precise values, but it is only available for calm water surfaces where coherent constructive scattering (specular reflection) is predominant. In the case of rougher surfaces, the reflected signal’s coherency is lost, and therefore the code-based algorithm must be used.
Figure 2. The geometry of GNSS signal reflections for altimetry applications.
The basic equation that links the delay of arrival of both signals with the height of the antennas over the surface as a function of time (t) can be written as equation (1):
(1)
where τ represents the lapse between the time of arrival of the reflected and the direct signals (as determined using either phase or code measurements), h is the height to be estimated, e is the elevation angle of the satellite considered, and b is the system bias, which is considered unknown but constant during every estimation. Solving a linear system with many such equations for different satellites over, say, one minute provides the sought estimation of h (and b).
Measuring the Level of a Water Reservoir
As mentioned before, when the water surface is sufficiently flat, the coherency of the reflected signal is maintained, thus its phase can be used to retrieve estimates of the height of the antennas over the surface. This algorithm is the so-called phase altimetry algorithm. The basic observable for this algorithm is the interferometric complex field (ICF), defined as the ratio between the reflected and direct complex correlation waveform peaks:
(2)
where PR and PD represent the time series of waveform peaks for the reflected and direct signals, respectively. In computing this ratio, adverse propagation effects such as the extra delay induced by the ionosphere and troposphere cancel out. Measuring the phase of the ICF, , one is then considering the phase single difference, , between the reflected and direct signals as given in equation (3):
(3)
where k is the wave number of the GPS carrier frequency (the reciprocal of the wavelength), noiseφ is the noise present in the ICF phase and is the unknown integer cycle ambiguity. D is the excess path of the reflected with respect to the direct signal, which can be directly linked to the height of the antennas over the surface. In order to solve for the cycle ambiguities, phase double differences among satellites are calculated, and by means of an ambiguity resolution algorithm (we use the null-space method developed by Manuel Martín-Neira and colleagues) the unknown phase-cycle ambiguities can be determined. It is then a straightforward procedure to work out the excess path of the reflected signals to finally deduce the height of the antennas over the water surface.
La Baells Experiment. An experimental campaign was carried out with an Oceanpal instrument at the La Baells water reservoir (near Berga in Catalonia, Spain) in cooperation with the Catalan Water Agency. This experiment was designed to study the feasibility of accurate altimetry measurements at lakes and reservoirs using our technique.
Within this campaign, one week of data was gathered early in March 2008 to compare the Oceanpal GNSS-R phase-altimetry measurements with those from the La Baells in-situ sensor (a water bubbler known to have centimeter-level accuracy). The results from this campaign are shown in FIGURE 3. After referencing the measurements to the antennas’ position with respect to the mean water level, the accuracy obtained from the Oceanpal measurements with respect to the ground truth (the water bubbler) was better than 2 centimeters (after a five-minute integration time).
Figure 3. Results of a one-week campaign at the La Baells water reservoir near Berga, Spain, in March 2008. Lake height in meters with respect to mean sea level.
Despite the fact that the phase altimetry algorithm is precise, it requires the simultaneous observation of several reflections from different satellites to converge and accurately solve for the phase ambiguities. However, this cannot be done for all scenarios, and in these situations the conventional phase altimetry algorithm cannot be applied.
Lake Laja Experiment. A case where we couldn’t use the phase approach was project Hydro. This was an initiative developed by our organization in collaboration with Pontificia Universidad Católica de Chile (the Pontifical Catholic University of Chile) and funded by ENDESA (Empresa Nacional de Electricidad S.A.), one of the world’s largest electricity companies. An Oceanpal instrument was installed at Lake Laja, in the Biobío Region, Chile, a water reservoir managed by ENDESA Chile. The Hydro project aims to use remote sensing assets to predict and monitor water flow in the Laja River basin. For that, having precise measurements of Lake Laja’s level is a must.
The instrument was installed on the shore of the lake as seen in FIGURE 4. However, the high variability of the lake’s level, more than 10 meters in one year, and the abruptness of the terrain, results in the number of observed reflections from the water surface being quite low. This is especially the case when the level of the lake is low. In this situation, the number of different GPS satellites observed per hour was calculated to be fewer than two for more than 45 percent of the time, and fewer than three for more than 85 percent of the time. Given this scarcity of reflections, we could not use the phase altimetry algorithm as described above.
Figure 4. The Oceanpal installation at Lake Laja, Chile.
We developed a new phase altimetry algorithm, which considers the interferometric phase evolution over time. The resulting relative phase parameter can be linked to the height of the antennas over the water surface by means of the same geometrical relationship as before. Despite the fact that measuring a relative phase increases the measurement noise with respect to the case in which an absolute phase is used, the phase ambiguity and the bias between the direct and reflected receiving channels do not need to be calculated, thus reducing the complexity of the algorithm and its convergence requirements. A Kalman filter is used to smooth the inherently noisy behavior of the relative phase.
The Oceanpal measurements were compared to those of a sensor operated by the Dirección General de Aguas (DGA), the Chilean water management agency. An accuracy better than 9 centimeters was achieved in determining the lake’s level during the austral winter, when the lake is at its minimum level and therefore the satellites’ reflections from the water surface are scarce. The lake level has its maximum during the summer after the melting season. During this period of time, the achieved accuracy of Oceanpal with the new phase algorithm was better than 5 centimeters. A comparison of Oceanpal and DGA’s sensor measurements of the water level is shown in FIGURE 5.
Figure 5A. A comparison of measurements of Lake Laja’s water level by Oceanpal and a water bubbler sensor operated by Dirección General de Aguas (DGA) for two periods of time corresponding to the austral winter (from late April 2009 until early August 2009).Figure 5B. A comparison of measurements of Lake Laja’s water level by Oceanpal and a water bubbler sensor operated by Dirección General de Aguas (DGA) for two periods of time corresponding to the austral summer (from late November 2009 until late January 2010).
Measuring Sea Level
Sea level is obtained from Oceanpal measurements by means of the code altimetry algorithm due to the inherent roughness of the sea surface. This technique derives altimetric information from the displacement of reflected waveforms with respect to the direct ones. Such a displacement can be directly related to the delay between the direct and reflected signals (the so-called lapse), and is used in a similar way to the phase-based method to extract the altimetry information of the water surface being monitored.
Despite the fact that the code altimetry algorithm is not as precise as the phase altimetry algorithm, it is not subject to the coherence requirement for the reflected signal. Therefore, it can be applied to rough, dynamic surfaces such as the open ocean and coastal areas. The use of code altimetry in rough water conditions results in a clear observation of tide dynamics but, as expected, with a higher error range compared to situations where phase altimetry can be applied.
Scheveningen Pier Experiment. The performance of the code-based algorithm was tested during an experimental campaign carried out on Scheveningen Pier in Den Haag (The Hague), The Netherlands. An Oceanpal instrument was installed close to a Radac X-band radar tide gauge. FIGURE 6 shows the tide variation at the installation site estimated by the Radac instrument and by Oceanpal. As can be seen, a good agreement between both estimates is achieved with a standard deviation of the difference of 12 centimeters.
Figure 6. Daily tidal variation at Scheveningen Pier, The Hague, The Netherlands, on May 3-4, 2008, measured by X-band radar and Oceanpal.
To improve this result, a combination of code and phase estimation is being investigated, involving the alignment of the phase using the code information. The combination of these two parameters may provide the best of both worlds. However, with the signals from modernized GPS and those of the forthcoming Galileo system, the code-ranging precision is envisioned to increase by a factor of four or five, which is expected to impact directly on the precision of the code altimetry algorithm.
Conclusion and Outlook
During the past decade, the scientific community’s interest in GNSS-R has grown, leading to the continuous development of new applications and to an increasing relevance in specific market niches. Some of these applications, especially those related to the monitoring of water surfaces, have reached an operational level of maturity, and provide end users with valuable information.
In this brief article, we have described the Oceanpal instrument and outlined its use in altimetric measurements of water surfaces. It was shown that using the phase of reflected signals with respect to that of direct signals, accurate measurements of a lake’s level could be obtained. In addition, we overviewed a new algorithm that incorporates the evolution of this phase in time. This algorithm is suitable for low satellite visibility scenarios. For example, using this algorithm, the level of Lake Laja in Chile was determined with an overall accuracy better than 7 centimeters. Such a level of accuracy meets the monitoring requirements necessary for improving the stream-flow prediction in the Laja River basin. We also showed that code altimetry can be successfully used to monitor sea level variations associated with tides, with a demonstrated accuracy of 12 centimeters.
These encouraging results are expected to be further improved with the evolution of GPS, the refurbishment of the Russian GLONASS system, and the deployment of the European Galileo system. First of all, when all three navigation systems are fully deployed, it is calculated that at least 20 navigation satellites will be visible at the same time. A GNSS-R instrument could take advantage of this large number of available signals. In addition, the quality of these signals is expected to be largely improved in terms of signal-to-noise ratio, bandwidth, and ranging precisions, which will in turn improve the performance of GNSS-R altimetry algorithms. As a result, the prospects for GNSS-R altimetry over water surfaces, not only for ground-based systems, but also airborne and even spaceborne systems, are extremely promising.
Manufacturers
The Oceanpal instrument was developed by Starlab, Barcelona, Spain. The Scheveningen Pier experiment used a Radac, Haarlem, The Netherlands, WaveGuide radar level gauge.
ALEJANDRO EGIDO has a B.Sc. degree in electrical engineering from the University of Zaragoza, Spain. After his studies, he worked on the Sentinel-1 remote sensing satellite project at the European Space Agency (ESA), where he performed the interference analysis of the synthetic aperture radar instrument. Since 2007, he has been a research engineer at Starlab, Barcelona, while pursuing a Ph.D. at the Polytechnic University of Catalonia. His main research field is the use of GNSS signals as sources of opportunity for remote sensing applications, with special interest in estimating terrestrial bio-geophysical parameters.
MARCO CAPARRINI received the “Laurea” degree in electronic engineering — remote sensing from the University “La Sapienza” in Rome. He has worked as a research engineer at ESA’s European Space Research and Technology Centre in Noordwijk, The Netherlands; at the German Aerospace Center in Oberpfaffenhofen, Germany; and at the Swiss Federal Institute of Technology in Zurich. His main research field is the use of GNSS signals as sources of opportunity for remote sensing of planet Earth, and he is the Starlab manager for the space research and development area.
FURTHER READING
• Principles of GNSS Reflectometry (GNSS-R)
“The PARIS Concept: An Experimental Demonstration of Sea Surface Altimetry Using GPS Reflected Signals” by M. Martín-Neira, M. Caparrini, J. Font-Rossello, S. Lannelongue, and C. Serra Vallmitjana in IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 1, January 2001, pp. 142–150, doi: 10.1109/36.898676.
• Overview of GNSS-R Applications
“GNSS Reflectometry and Remote Sensing: New Objectives and Results” by J. Shuanggen and A. Komjathy in Advances in Space Research, Vol. 46, 2010, pp. 111–117, doi:10.1016/j.asr.2010.01.014.
• GNSS-R Experimental Campaigns
“Oceanpal: Monitoring Sea State with a GNSS-R Coastal Instrument” by M. Caparrini, A. Egido, F. Soulat, O. Germain, E. Farrès, S. Dunne, and G. Ruffini in Proceedings of the 2007 International Geoscience and Remote Sensing Symposium, Barcelona, Spain, July 23–28, 2007, pp. 5080–5083.
“The Eddy Experiment: Accurate GNSS-R Ocean Altimetry from Low Altitude Aircraft” by G. Ruffini, F. Soulat, M. Caparrini, O. Germain, M. Martín-Neira in Geophysical Research Letters, Vol. 31, L12306, 4 pp., 2004, doi:10.1029/2004GL019994.
“The Eddy Experiment: GNSS-R Speculometry for Directional Sea- Roughness Retrieval from Low Aircraft” by O. Germain, G. Ruffini, F. Soulat, M. Caparrini, B. Chapron, and P. Silvestrin in Geophysical Research Letters, Vol. 31, L21307, 4 pp., 2004, doi: 10.1029/2004GL020991.
“Wind Speed Measurement Using Forward Scattered GPS Signals” by V. Zavorotny, J. Garrison, A. Komjathy, and S. Katzberg in IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 1, January 2002, pp. 50–65, doi: 10.1109/36.981349.
• GNSS-R for Monitoring Soil Moisture
“The SAM Sensor: An Innovative GNSS-R System for Soil Moisture Retrieval” by A. Egido, C. Martin-Puig, D. Felip, M. Garcia, M. Caparrini, E. Farrés, and G. Ruffini in Proceedings of NAVITEC 2008, the 4th ESA Workshop on Satellite Navigation User Equipment Technologies, Noordwijk, The Netherlands, December 10–12, 2008.
“GPS Ambiguity Resolution and Validation: Methodologies, Trends and Issues” by D. Kim and R.B. Langley in Proceedings of the 7th GNSS Workshop – International Symposium on GPS/GNSS, Seoul, Korea, Nov. 30 – Dec. 2, 2000, Tutorial/Domestic Session, pp. 213–221.
The first time I ever heard of the Magnavox Research Laboratory in Torrance, California, was in 1966, as a young engineer working at Hughes Aircraft. We were building large (46-foot diameter) ground stations for the Defense Satellite Communications System (DSCS). Magnavox was supplying the secret anti-jam modems used in the terminals.
Because of this, I also learned a little about spread-spectrum pseudo-noise (PN), something quite esoteric at the time and not taught in engineering school. I noticed a widespread respect for Magnavox from my colleagues who referred to the company and its equipment as “Magicbox.”
Within a year I had transferred to the Hughes division responsible for developing satellites. We were working on a study known as 621B, for using satellites for positioning. Our teammate for the study was Magnavox. That team was responsible for the payload signal design, for which the team chose PN as the modulation to provide for multiple access, ranging, data transmission, and anti-jam.
Before long, my boss decided to leave Hughes and go work for Magnavox. He took two of his systems engineers with him. I was one of them.
In1968, the U.S. Air Force could not yet sell the 621B concept as an Advanced Development Program, so instead opted to experiment and prove that PN modulation could be used to accurately measure a half-mile of cable. Hughes bowed out since there wasn’t any satellite procurement in the offing. Magnavox and the other 621B contractor, TRW, each took on the challenge of measuring the cable.
Where Hughes had been 10-deep in Ph.D.s in every discipline, Magnavox was 10-deep in PN experts, which I believe at that time was the world’s majority. Thus it was natural for the Air Force to ask them to continue, and develop a receiver to be used in the next phase of 621B. An inverted range was set up with four PN transmitters, and an aircraft with the receiver and a bottom antenna flew over them. The aircraft’s position was determined using the PN range measurements and the known locations of the transmitters. The data from that receiver, called the MX450, was used to help justify the Department of Defense (DoD) decision to proceed into the Advanced Development Phase of GPS. Some of the people who contributed to this were named in Dr. Brad Parkinson’s recent articles on the origins of GPS. During that time I was working on the next generation of spread-spectrum modems for the DSCS.
Magnavox went on to develop these PN satcom modems for all three services, and thus was a natural choice to develop the first military GPS receivers (known as X and Y sets and the first Manpack), as well as the first C/A receiver, the Z set, and the very first spaceborne receiver called GPSPAC.
As soon as we completed the first Manpack, I approached Col. Paul Weber, the Joint Program Office Army Deputy Program Manger, and asked if he would pose with the Manpack on his back for a brochure we wanted to produce to show to potential Army and Marine Corps users. He agreed, dressed in his combat uniform, and went with our photographer into the wild woods of San Pedro (near the Port of Los Angeles) for the picture shown in the brochure.
Magnavox also developed the military GPS Engineering Models in competition with Rockwell Collins. Magnavox lost the production contract to Rockwell Collins a year after I left to join IEC, now known as L-3 Communications.
Magnavox also pioneered commercial GPS sets for use in the marine and survey markets. Today, you will still find many of the original GPS user equipment developers still at it as consultants and engineers at Raytheon, Navcom, Trimble, IEC, and others. Perhaps our most famous alumni is Dr. Min Kao, the “min” of Garmin.
Len Jaconbson
LEN JACOBSON is a consultant to the GPS industry and has served as an expert witness in many legal proceedings involving GPS. He is the author of the book GNSS Markets and Applications, published in 2007, and is a longstanding member of this magazine’s Editorial Advisory Board.
I wish to second Jim Spilker’s comments in his recent letter to the editor regarding the two wonderful GPS history articles by Brad Parkinson. My endorsement of his comments also includes those about the origins of the L5 signal with reference to the 1999 paper by Spilker and Van Dierendonck, “Proposed New Civil GPS Signal at 1176.45 MHz.” Jim commented in the letter that “. . . . the work I did in designing the GPS L5 signal was performed as a gift to the U.S. Air Force, Federal Aviation Administration, and our country, . . .” It was a generous contribution, and I applaud it.
However, this leads me to comment on other very important but underreported gifts to L5 and subsequent signal developments. A small indication of the L5 contributions is given in the brief acknowledgement at the end of the referenced paper, “The authors wish to acknowledge the contributions of Dr. C.R. Cahn and Thomas Stansell in the selection of this signal.” It also is important to recognize that the L5 signal structure was formulated within an RTCA committee of mostly volunteers. Among other key participants, in addition to A.J., was Dr. Chris Hegarty.
The L5 signal design included several innovations which influenced subsequent development of modernized GPS signals and of signals for other GNSS. My ranking of the most important L5 innovations is:
Center frequency of 1176.45 MHz in an ARNS band
Two signal components, one with a data message and the other without (pilot signal)
Forward error correction (FEC) (first GPS use, borrowed from WAAS)
Overlay code to frame symbols and eliminate need for bit synchronization
CNAV message structure for better accuracy and more flexibility
The list doesn’t include the 10.23 MHz code clock rates or having two signals in phase quadrature, which were included in the first GPS satellite launched in 1978. The new center frequency was recommended by Karl Kovach (then with ARINC and now with Aerospace) and adopted before the signal design began, but it was central to the L5 purpose of having a civil signal in an ARNS band. This same center frequency also will be provided by Galileo and Compass, so it was a vital innovation. Although forward error correction had been adopted for WAAS, the first use on GPS was the L5 design. In one form or another, it too will be used on most if not all other GNSS signals.
The second and fourth innovations in the list above were contributed by Dr. Charlie Cahn with help and encouragement from Richard (Rich) Keegan and myself. Having a dataless or pilot signal provides a significant boost to performance and has been adopted for almost every subsequent GNSS signal. The problematic C/A bit synchronization process has been eliminated by the data symbol overlay code (or equivalent) in all subsequent signals. The CNAV message format was principally developed by Karl Kovach with significant help from Art Dorsey of Lockheed Martin.
In summary, Brad Parkinson helped memorialize many of the early “GPS Heroes” who made GPS what it is today. Other heroes have contributed to GPS modernization, and credit should be given where credit is due. Brad mentioned Charlie Cahn, one of my real heroes, who helped shape the 621B and early GPS signals and has continued to contribute in many ways. In addition to the very significant innovations mentioned above, Charlie was key to similar improvements made in the subsequent L2C, M-code, and L1C signal designs.
— Tom Stansell
Kauai, Hawaii
An Advisor Bids Farewell
Paul Cross
Many thanks for the kind invitation to GPS World’s Leadership Dinner. I have to decline as I won’t attend ION-GNSS this year. I will retire from University College London at the end of September. I don’t plan to remain active in the world of GNSS after my retirement so this would be a good time for me to step down from the Editorial Board. I’ve very much enjoyed my association with GPS World and have benefited enormously from it.
I wish you and the magazine continued success. You have come a long way over the past twenty years or so and you are now, and have been for some time, the premier source of news (and very useful gossip!) relating to GNSS worldwide. I don’t know anyone of any significance who doesn’t read GPS World every month. Your highly accessible technical articles have been of enormous help to many cohorts of students here at UCL, and all over the world.
Earlier today (August 31), I conducted a webinar entitled “Solar Activity, SBAS and 24+3 GPS Constellation Updates.” Considering we only announced the webinar three weeks ago, we had a fantastic registration numbers, with more than 570 registered. Thank you for attending if you did. If you weren’t able to you’ll be able to download the presentation by registering here. After registering, you’ll be notified when it’s available for download (usually a couple of days after the webinar).
I had a lot of questions before and during the webinar. As customary, I’d like to address some of those as well as present the poll results here. First, the poll questions and results with accompanying pie charts to illustrate the results.
Poll #1: How concerned are you about solar activity affecting your GNSS operations?
Total votes: 157
Gakstatter comment: These numbers don’t surprise me. Personally, I probably fall in the “Somewhat” category, but my GPS/GNSS field work is pretty flexible so I can easily adjust without much inconvenience. However, if I had several crews using GPS/GNSS on a daily or near-daily basis or I had equipment relying on GPS/GNSS, I think I’d be in the “Very” category because the $$ impact would be much higher.
Poll #2: If it was available, would you be interested in receiving alerts/warnings of solar activity that may affect GNSS operations?
Total votes: 176
Gakstatter comment: I’m not surprised at these results either. When I initially considered this poll, I was thinking about asking which type of platform you would prefer to receive alerts/warnings with the choices being Droid app, iPhone app, Blackberry app, text message, e-mail, etc. If you have a preference on that, fire off a quick e-mail to me. Secondly, a few of you pointed out that NASA has an app for this, but keep in mind that the system I’m considering is focused specifically on high-performance/precision GPS/GNSS users, which would eliminate a lot of the baggage of the alert/warning systems available today. Poll #3: Do any of your GPS receivers use SBAS (WAAS/EGNOS/MSAS) as a primary source of corrections?
Total votes: 115
Gakstatter comment: Not much to say here except that a substantial number of commercial GPS users are relying on SBAS. This has definitely been the trend over the past five years.
Poll #4: Do you expect that the GPS 24+3 configuration will improve your GPS productivity?
Total votes: 172
Gakstatter comment: Like most of you, I have great expectations for the 24+3 configuration. While launching more satellites with L5 would be nice, that’s a long-term effort, whereas the 24+3 configuration is something we will benefit from in a few months and are seeing some marginal benefit now. In January 2011, once all the satellites have arrived at their destination slots, I’ll plot new visibility charts and see where we stand.
Following are some of the questions that were posed by the audience during the webinar: Question #1: The blueline ends in late 2009. Any information on up-to-date activity?
Gakstatter comment: This question was in reference to the Solar Cycle 24 prediction chart I displayed. The chart was probably small and difficult to read when displayed on your computer. Here’s a larger version of it. This was a chart released by the NOAA Space Weather Prediction Center in May 2009. Although sunspots don’t directly affect GPS operations, there is some relationship between sunspots and geomagnetic storms. Below it is an updated chart with actual values through the end of July 2010.
Question #2: What tools/online sites can be used to see if there is a TEC anomaly at a specified time, including “today”?
Gakstatter comment: There is a cool real-time chart of the U.S. on NOAA’s Space Weather Prediction Center website. There are other interesting charts on SWPC’s website like the 10-day trend chart. The JPL had a website that displayed a real-time TEC, but I just checked it and it hasn’t been updated since June. Another website to check is the National Satellite Test Bed that displays a real-time plot of the WAAS ionospheric grid points. Click here to view a global real-time (updated every 60 minutes) TEC chart of the world published by the Australian Space Weather Agency.
Question #3: What is better for a receiver, Differential GPS or dual frequency? Any references on this?
Gakstatter comment: With respect to performance during periods of heightened solar activity, definitely dual-frequency receivers. Although I don’t have a specific cite for you right now, there has been plenty written on this subject. Single frequency DGPS receivers are the most vulnerable during periods of heightened solar activity.
Question #4: Is the disruption in the sub-meter scale, single-digit meters, or tens of meters?
Gakstatter comment: It depends on the severity of the geomagnetic storm. During the worst times of the Oct. 2003 event, it was up to 25 meters. That order of magnitude would be rare. Remember, those events occurred in about four days over the 11-year cycle. I have some figures that relate TEC to position error, but I’ll withhold those until I’ve got a better understanding of how practical they are.
Question #5: Is there some type of notification system for GNSS users of major solar events? E-mail alerts? Twitter tweets?
Gakstatter comment: Following are instructions for signing up for the NOAA alerts/warnings. This is a good start. Stay tuned for my alert/warning system later this fall. Follow me on Twitter at http://twitter.com/GPSGIS_Eric
Following are detailed instructions for signing up for alerts:
-Click on Email products (under the Support Services menu on the left)
-Create an account if you don’t have one already (it’s free).
-Click on Subscribe
You don’t want to subscribe to everything. Here are the ones specific for GPS operations:
-Advisories/Space Weather Bulletin
-Geomagnetic Storm Products/(sign up for both Alerts and Warnings for K6, K7, K8, K9 events.
-For high latitude (55 degrees and higher) users, als
o sign up for Alerts and Warnings for K4 and K5 events.
Question #6: There is already an iPhone/iPod application that gives alerts of solar activity.
Gakstatter comment: Yes, I’m aware of the NASA app and there maybe others, but in my opinion they are too broad for high-performance/high-precision GPS/GNSS users. Personally, I don’t need to know about new sunspots and where they are located on the sun (although it’s cool to see in that app). I need to know when geomagnetic events are occurring that may interrupt or affect my GPS/GNSS fieldwork.
Question #7: Ouch, we’re at 59 degrees north, and 134 west. Seems like these problems are “picking” on Juneau.
Gakstatter comment: The good news for you is that Alaska has the most dense concentration of WAAS Reference Stations in the entire WAAS coverage area. Well, maybe not Juneau, but certainly “mainland” Alaska :-). Seriously, parts of Alaska produce the best WAAS accuracy due to the high density of WAAS reference stations.
Question #8: Will parts of BC, Canada, be affected by the SBAS outage?
Gakstatter comment: Not really, except that you’ll have one less WAAS GEO satellite in view for a month or so until PRN 133 is operational in November. I don’t think you’ll notice any change in performance. The exception would be if your receiver uses SBAS ranging. In that case, you’d be tracking one less satellite between the time that PRN 135 becomes unusable and the time PRN 133 becomes operational.
Following is an elevation plot of the current WAAS GEO satellites (PRN 135 and PRN 138):
Following is an elevation plot of only PRN 138. This is a possible scenario after PRN 135 is unusable in October 2010 and before PRN 133 is placed into service in November 2010.
Following is an elevation plot of PRN 138 and the new PRN 133 GEO which is expected to be placed into service sometime in November 2010.
Question #9: With the 24+3 configuration, is it that some sats were flying almost in tandem and they are spreading them out more?
Gakstatter comment: Yes, that is essentially what is happening. Some believe, including me, that a 24+6 configuration would be even better! But, one step at a time. I feel good that the U.S. Air Force is listening and responding.
I addressed many of the questions from the webinar. Some will take a little research on my side to answer properly. I should be able to address those in the mid-September newsletter. Thanks again to those who registered for the webinar. Feel free to send me an e-mail any time with comments, suggestions or questions.
A performance assessment demonstrates the ability of a networked group of users to locate themselves and each other, navigate, and operate under adverse conditions in which an individual user would be impaired. The technique for robust GPS positioning in a dynamic sensor network uses a distributed GPS aperture and RF ranging signals among the network nodes.
By Dorota A. Grejner-Brzezinska, Charles Toth, Inder Jeet Gupta, Leilei Li, and Xiankun Wang
In situations where GPS signals are subject to potential degradations, users may operate together, using partial satellite signal information combined from multiple users. Thus, collectively, a network of GPS users (hereafter referred to as network nodes) may be able to receive sufficient satellite signals, augmented by inter-nodal ranging measurements and other sensors, such as inertial measurement unit (IMU), in order to form a joint position solution.
This methodology applies to numerous U.S. Department of Defense and civilian applications, including navigation of dismounted soldiers, emergency crews, on-the-fly formation of robots, or unmanned aerial vehicle (UAV) swarms collecting intelligence, disaster or environmental information, and so on, which heavily depend on availability of GPS signals. That availability may be degraded by a variety of factors such as loss of lock (for example, urban canyons and other confined and indoor environments), multipath, and interference/jamming. In such environments, using the traditional GPS receiver approach, individual or all users in the area may be denied the ability to navigate.
A network of GPS receivers can in these instances represent a spatially diverse distributed aperture, which may be capable of obtaining gain and interference mitigation. Further mitigation is possible if selected users (nodes) use an antenna array rather than a single-element antenna. In addition to the problem of distributed GPS aperture, RF ranging among network nodes and node geometry/connectivity forms another topic relevant to collaborative navigation. The challenge here is to select nodes, which can receive GPS signals reliably, further enhanced by the distributed GPS aperture, to serve as pseudo-satellites for the purpose of positioning the remaining nodes in the network.
Collaborative navigation follows from the multi-sensor navigation approach, developed over the past several years, where GPS augmentation was provided for each user individually by such sensors as IMUs, barometers, magnetometers, odometers, digital compasses, and so on, for applications ranging from pedestrian navigation to georegistration of remote sensing sensors in land-based and airborne platforms.
Collaborative Navigation
The key components of a collaborative network system are
inter-nodal ranging sub-system (each user can be considered as a node of a dynamic network);
optimization of dynamic network configuration;
time synchronization;
optimum distributed GPS aperture size for a given number of nodes;
communication sub-system; and
selection of master or anchor nodes.
Figure 1 illustrates the concept of collaborative navigation in a dynamic network environment. Sub-networks of users navigating jointly can be created ad hoc, as indicated by the circles. Some nodes (users) may be parts of different sub-networks.
FIGURE 1. Collaborative navigation concept.
In a larger network, the selection of a sub-network of nodes is an important issue, as in case of a large number of users in the entire network, computational and communication loads may not allow for the entire network to be treated as one entity. Still, information exchange among the sub-networks must be assured.
Conceptually, the sub-networks can consist of nodes of equal hierarchy or may contain master (anchor) nodes that normally have a better set of sensors and collect measurements from all client nodes to perform a collaborative navigation solution. Table 1 lists example sensors and techniques that can be used in collaborative navigation.
TABLE 1. Typical sensors for multi-sensor integration: observables and their characteristics, where X,Y,Z are the 3D coordinates, vx, vy, vz are the 3D velocities,
The concept of a master node is also crucial from the stand point of distributed GPS aperture, where it is mandatory to have master nodes responsible for combining the available GPS signals.
Master nodes or some selected nodes will need anti-jamming protection to be effective in challenged electromagnetic (EM) environments. These nodes may have stand-alone anti-jamming protection systems, or can use the signals received by antennas at various nodes for nulling the interfering signals.
Research Challenges
Finding a solution that renders navigation for every GPS user within the network is challenging. For example, within the network, some GPS nodes may have no access to any of the satellite signals, and others may have access to one or more satellite signals. Also, the satellite signals received collectively within the network of users may or may not have enough information to determine uniquely the configuration of the network.
A methodology to integrate sensory data for various nodes to find a joint navigation solution should take into account:
acquisition of reliable range measurements between nodes (including longer inter-nodal distances);
limitation of inter-nodal communication (RF signal strength);
assuring time synchronization between sensors and nodes; and
limiting computational burden for real time applications.
Distributed GPS Apertures
In the case of GPS signal degradation due to increased path loss and radio frequency interference (RFI), one can use an antenna array at the receiver site to increase the gain in the satellite signal direction as well as steer spatial nulls in the interfering signal directions. For a network of GPS users, one may be able to combine the signals received at various receivers (nodes) to achieve these goals (beam pointing and null steering); see Figure 2.
Figure 2. Distributed antenna array.
However, a network of GPS users represents a distributed antenna aperture with large (hundreds of wavelengths) inter-element spacing. This large thinned antenna aperture has some advantage and many drawbacks. The main advantage is increased spatial resolution which allows one to discriminate between signals sources with small angular separations. The main drawback is very high sidelobes (in fact, grating lobes) which manifest as grating nulls (sympathetic nulls) in null steering. The increased inter-element spacing will also lead to the loss of correlation between the signals received at various nodes. Thus, space-only processing will not be sufficient to increase SNR by combining the satellite signals received at various nodes. One has to account for the large delay between the signals received at various nodes.
Similarly, for adaptive null steering, one has to use space-time adaptive processing (STAP) for proper operation. These research challenges must be solved for distributed GPS aperture to become a reality:
Investigate the increase in SNR that can be obtained by employing distributed GPS apertures (accounting for inaccuracies in the inter-nodal ranging measurements).
Investigate the improvement in the signal-to-interference-plus-noise ratio (SINR) that can be obtained over the upper hemisphere when a distributed GPS aperture is used for adaptive null steering to suppress RFI in GPS receivers. Obtain an upper bound for inter-node distances.
Based on the results of the above two investigations, develop approaches for combined beam pointing and null steering using distributed GPS apertures.
Inter-Nodal Ranging Techniques
In a wireless sensor network, an RF signal can be used to measure ranges between the nodes in various modes. For example, WLAN observes the RF signal strength, and UWB measures the time of arrival, time difference of arrival, or the angle of arrival. There are known challenges, for example, signal fading, interference or multipath, to address for a RF-based technique to reliably serve as internodal ranging method.
Ranging Based on Optical Sensing. Inter-nodal range measurements can be also acquired by active and passive imaging sensors, such as laser and optical imaging sensors. Laser range finders that operate in the eye-safe spectrum range can provide direct range measurements, but the identification of the object is difficult. Thus, laser scanners are preferred, delivering 3D data at the sensor level. Using passive imagery, such as digital cameras, provides a 2D observation of the object space; more information is needed to recover 3D information; the most typical techniques is the use of stereo pairs or, more generally, multiple-image coverage. The laser has advantages over optical imagery as it preserves the 3D object shapes, though laser data is more subject to artifacts due to non-instantaneous image formation.
In general, regardless whether 2D or 3D imagery is used, the challenge is to recognize the landmark under various conditions, such as occlusions and rotation of the objects, when the appearance of the landmark alternates and the reference point on the landmark needs to be accurately identified, to compute the range to the reference point with sufficient accuracy.
Network Configuration
Nodes in the ad hoc network must be localized and ordered considering conditions, such as type of sensors on the node (grade of the IMU), anti-jamming capability, positional accuracy, accuracy of inter-nodal ranging technique, geometric configuration, computational cost requirements, and so on. There are two primary types of network configurations used in collaborative navigation: centralized and distributed.
Centralized configuration is based on the concept of server/master and client nodes.
Distributed configuration refers to the case where nodes in the network can be configured without a master node, that is, each node can be considered equal with respect to other nodes.
Sensor Integration
The selection of data integration method is an important task; it should focus on arriving at an optimal solution not only in terms of the accuracy but also taking the computational burden into account. The two primary options are centralized and decentralized extended Kalman filter (EKF).
Centralized filter (CF) represents globally optimal estimation accuracy for the implemented system models.
Decentralized filter (DF) is based on a collection of local filters whose solutions can be combined by a single master filter. DFs can be further categorized based on information-sharing principles and implementation modes.
Centralized, Decentralized EKF. These two methods can provide comparable results, with similar computational costs for networks up to 30 nodes. Figures 3–5 describe example architectures of centralized/decentralized EKF algorithms.
In Figure 3, all measurements collected at the nodes and the inter-nodal range measurements are processed by a single centralized EKF. Figures 4 and 5 illustrate the decentralized EKF with the primary difference between them being in the methods of applying the inter-nodal range measurements. The range measurements are integrated with the observations of each node by separate EKF per node in Figure 4, while Figure 5 applies the master filter to integrate the range measurements with the EKF results of all participating nodes.
To provide a preliminary performance evaluation of an example network operating in collaborative mode, simulated data sets and actual field data were used. Figure 6 illustrates the field test configuration, showing three types of nodes, whose trajectories were generated and analyzed.
FIGURE 6. Collaborative navigation field test configuration.
Nodes A1, A2, and A3 were equipped with GPS and tactical grade IMU, node B1 was equipped with GPS and a consumer grade IMU, and node C1 was equipped with a consumer grade IMU only. The following assumptions were used: all nodes were able to communicate; all sensor nodes were time-synchronized; nodal range measurements were simulated from GPS coordinates of all nodes; and the accuracy of GPS position solution was 1–2 meters/coordinate (1s); the accuracy of inter-nodal range measurements was 0.1meters (1s); all measurements were available at 1 Hz rate; the distances between nodes varied from 7 to 70 meters.
Individual Navigation Solution. To generate the navigation solution for specific nodes, either IMU or GPS measurements or both were used. Since the reference trajectory was known, the absolute value of the differences between the navigation solution (trajectory) and the reference trajectory (ground truth) were considered as the navigation solution error. Figure 7 illustrates the absolute position error for the sample of 60 seconds of simulated data, with a 30-second GPS outage for nodes A1, A2, A3 and B1 (node C1 is not shown, as its error in the end of the test period was substantially bigger than that of the remaining nodes. Table 2 shows the statistics of the errors of each individual node’s trajectory for different sensor configurations.
FIGURE 7. GPS/IMU positioning error for A1, A2, A3, B1 (includes a 30-second GPS outage.)
Collaborative Solution. In this example, collaborative navigation is implemented after acquiring the individual navigation solution of each node, which was estimated with the local sensor measurements. The collaborative navigation solution is formed by integrating the inter-nodal range measurements to other nodes in a decentralized Kalman filter, which is referred to as “loose coupling of inter-nodal range measurements.” The test results of different scenarios are listed in Table 3. For cases labeled “30-sec GPS outage,” the GPS outage is assumed at all nodes that are equipped with GPS. The results listed in Table 3 indicate a clear advantage of collaborative navigation for nodes with tactical and consumer grade IMUs, particularly during GPS outages. When GPS is available (see, for example, node A1) the individual and collaborative solutions are of comparable accuracy.
The next experiment used tight coupling of inter-nodal range measurements at each node’s EKF in order to calibrate observable IMU errors even during GPS outages. In addition, varying numbers of master nodes are considered in this example. The tested data set was 600 seconds long, with repeated simulated 60-second GPS gaps, separated by 10-second periods of signal availability. The inter-nodal ranges were ~20 meters.
Table 4 and Figure 8 summarize the navigation solution errors for collaborative solution of node C1 equipped with consumer grade IMU only, supported by varying quality other nodes. The error of the individual solution for this node in the end of the 600-second period reach nearly 250 kilometers (2D). Even for the case with a single anchor node (A1), the accuracy of the 2D solution is always better than 2 meters. Another 900-second experimental data with repeated GPS 60-second gaps on B1 node was analyzed with inter-nodal ranging up to 150 meters. Table 5 summarizes the results for C1 node.
FIGURE 8. Absolute error for IMU-only and collaborative navigation solutions of C1 (GPS outage.)
Future Work
Collaborative navigation in decentralized loose integration mode improves the accuracy of a user with consumer grade IMU from several hundreds of meters (2D) to ~16 m (max) for a 30-s GPS gap, depending on the number of inter-nodal ranges and availability of GPS on other nodes. For a platform with GPS and consumer grade IMU (node B1) the improvement is from a few tens of meters to below 10 m.
Better results were obtained when tight integration mode was applied, that is, inter-nodal range measurements were included directly in each EKF that handles measurement data collected by each individual node (architecture shown in Figure 4). For repeated 60-second GPS gaps, separated by 10-second signal availability, collaborative navigation maintains the accuracy at ~1–2 meter level for entire 600 s tested for nodes C1 and B1.
Even though the preliminary simulation results are promising, more extended dynamic models and operational scenarios should be tested. Moreover, it is necessary to test the decentralized scenarios 1 and 2 (Figures 4–5) and then compare them with the centralized integration model shown in Figure 3. Ad hoc network formation algorithm should be further investigated.
FIGURE 9. Absolute errors in collaborative navigation solutions of C1.
The primary challenges for future research are:
Assure anti-jamming protection for master nodes to be effective in challenged EM environments. These nodes can have stand alone anti-jamming protection system, or can use the signals received by antennas at various nodes for nulling the interfering signals.
Since network of GPS users, represents a distributed antenna aperture with large inter-element spacing, it can be used for nulling the interfering signals. However, the main challenge is to develop approaches for combined beam pointing and null steering using distributed GPS apertures.
Formulate a methodology to integrate sensory data for various nodes to obtain a joint navigation solution.
Obtain reliable range measurements between nodes (including longer inter-nodal distances).
Assess limitations of inter-nodal communication (RF signal strength).
Assure time synchronization between sensors and nodes.
Assess computational burden for the real time application.
Dorota Grejner-Brzezinska is a professor and leads the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at The Ohio State University (OSU), where she received her M.S. and Ph.D. in geodetic science. Charles Toth is a senior research scientist at OSU’s Center for Mapping. He received a Ph.D. in electrical engineering and geoinformation sciences from the Technical University of Budapest, Hungary. Inder Jeet Gupta is a research professor in the Electrical and Computer Engineering Department of OSU. He received a Ph.D. in electrical engineering from OSU. Leilei Li is a visiting graduate student at SPIN Lab at OSU. Xiankun Wang is a Ph.D. candidate in geodetic science at OSU
I’m happy that last week’s article titled “Are You a Professional?” evoked responses from readers. I thought I’d share a couple of the responses I received. Also, I’ve included a good piece on using GIS for commercial real estate market research.
"Are You a Professional?" letter to the editor from an independent GIS consultant:
A comment on your piece on professional. I have generally thought of professional as a simple English word that contrasts with unprofessional, and that’s what I think you were saying, too. Only when I started working with people who have to be registered and licensed did I come to understand that some people associate being professional with being registered and/or licensed.
Part of the confusion may be the English language: the words profession and professional sound very related. I grew up with the idea that a profession is something requiring special education and training, and the examples were always doctors and lawyers and teachers and ministers. By this definition, house painting could be a profession for someone who applies effort to learning about all of the different products and their uses and when they will fail and so on.
Wikipedia gives this: "A profession is a vocation founded upon specialised educational training, the purpose of which is to supply disinterested counsel and service to others, for a direct and definite compensation, wholly apart from expectation of other business gain."
That part about disinterested counsel could be an important piece of the confusion/distinction/pride?
"Are You a Professional?" letter to the editor from a state government GIS Specialist:
In response to your article "Are You a Professional?" I would like to note that I work in state government. In civil service we have "professional" working titles and "secretarial" working titles. So, by default, I am considered a professional because of my particular title — which is a GIS Specialist. But personally, I feel that there is a difference between conducting oneself as "professional", and actually being a "professional." If you conduct yourself as a professional, using the word as an adverb, you could be considered as such, in any job you hold. There is a professional manner of dress and conduct required to elicit respect from both your colleagues, and your clientele. However, when using the word as a noun, a professional used to imply, though perhaps not by official definition, that a person had an advanced education, or extensive experience, to some degree. They may not hold a PhD, but they would probably hold some type of degree, or possess extensive years of service in a particular field. I have both a degree in Graphic Design and almost 20 years of experience in the mapping industry, so I feel I am a professional for a multitude of reasons (none of which involve salary, as that is really negligible at best).
Also, I can completely understand where Gretchen Peterson is coming from in terms of her issues with map design, because I have had similar moments of exasperation at the poor design aspects in maps containing very complex datasets. Having experience in both the design and the analytical aspects of mapping, I have a better understanding of both areas. And although I consider myself a professional, I would not consider myself an expert of either. I have created maps since the days of scribe coat and Leroy lettering guides. I have remained in the field through the various computerized incarnations of digital mapping, including command line driven Sun Microstations, to the current Windows driven applications we have today. One thing that did remain consistent through it all, was the aspect of map composition and design, which is very often overlooked. I feel some type of graphic design courses should be part of a required curriculum for a Cartography, or GIS major, at any university. Or, at the very least, as an elective listed along with the course of study. Another frustration I have with the industry is the lack of understanding, of both the technology and map design, on the part of the clients that require the work. There are those that only worry about the "eye candy" factors without understanding the work involved in the actual data. And there are those that don’t care if a map is almost illegible, because their main concern is the content of the data, as opposed to its visual interpretation. A person working in this industry should really be able to wear a variety of hats in order to completely convey their intentions to an audience with any type of data. It is necessary to understand both your medium, and your audience, to achieve the most understandable and artistically rendered presentation of such scientific information. It’s a true mix of science and art, and quite often grossly misunderstood.
Following is a short piece from Esri writer Karen Richardson. I first met Karen at Esri’s Redlands office in the mid-90’s. When discussing the issue of position accuracy with land surveyors, I often use the commercial real estate example to illustrate how GIS can be a powerful tool even if the spatial accuracy is not within a centimeter, or even a meter, or even five meters.
Using GIS to Improve Market Research in Commercial Real Estate
Edens & Avant owns, operates, and develops community-oriented shopping centers in primary markets throughout the East Coast. More than 130 centers in 14 states make up its portfolio. The company’s clients include regional and national retailers such as Fresh Market, Whole Foods, Publix, Starbucks, and Target. The success of the company’s shopping centers is based on generating the best mix of retailers and creating high-profile developments that are optimally aligned with neighborhood need and market opportunity. Edens & Avant is headquartered in Columbia, South Carolina, and has regional headquarters in Boston, Massachusetts; Washington, D.C.; Atlanta, Georgia; and Miami, Florida.
Seeing a Place through Data
Edens & Avant required a system to research markets and locations as well as a platform to quickly market that information to prospective retailers. Whether a retailer is looking to open a new store, add a second store, or move from across town, the company has to be ready with a strong case for the retailer to move into an existing shopping center or a new development. Purchasing one-off reports to research each shopping center becomes inefficient when dealing with hundreds of locations that have rapidly changing information like demographic data.
In addition, instead of banking on the promise of growth driven by the housing boom—the standard model a few years ago—developers must now develop projections based on less robust growth and more conservative economic projections. "Healthy shopping centers are the ones that are located in markets with a diverse workforce and good balance of daytime-to-household population," says David Beitz, director of geographic information systems (GIS), Edens & Avant. As a result, the company needs to analyze, aggregate, and display accurate demographic information on a daily basis.
Use the Find Similar feature to identify new markets
that are similar to markets in which retailers are already successfully operating
Better Decisions through Mapping
Edens & Avant uses Esri Business Analyst software on the desktop and online to help its clients make the most informed decisions. Clients can see and understand all information available for each shopping center location, including address, major roads, competition, population density, and growth. Business Analyst Online (BAO) is used to generate a customized six-page report annually for each shopping center that is then used by investment leasing and development group agents so they can better visualize and understand their markets. The software helps identify new markets that are similar to those in which the retailers are already successfully operating. If staff members need customized reports or maps, they can request them from the GIS group.
Integration with Bing Maps provides monthly updates to aerial, road, and hybrid (aerial with labels) maps. "Using Business Analyst and Bing Maps, we are able to find locations fast," says Beitz. "Being able to view aerial images allows us to give a better context to our clients about location. This is particularly helpful when looking at larger areas."
The company looks carefully at optimizing its shopping center portfolio by selling properties in secondary and tertiary markets and buying properties in primary markets with dense populations in core-based statistical areas (CBSAs). Business Analyst is used to look at daytime population, income changes, and population changes, among other information. "It is very important to know the demographics in order to find areas that will perform best in this new economic climate," says Beitz.
Imagery combined with GIS software and other data make it easier to find the best store placement for retailers
Combining city data with updated demographic data ensures Edens & Avant has the most current information for their clients
Results
Edens & Avant can now serve its clients’ needs internally without outsourcing to third parties. They can research markets and assist in quickly leasing space by providing spatial information via maps and reports that uniquely characterize neighborhoods and are specific to each retailer. The ability to combine city building permit data ensures that Edens & Avant has the most current information for its clients. As a result, two planned grocery-anchored shopping centers are going forward in areas where population doubled even though residential construction recently slowed down. Being able to find and track this growth with Business Analyst allowed the company to minimize the carry time of the land and provide the shopping center sites based on the retailers’ timelines. Concludes Beitz, "Without the information to support these decisions and an accurate and appropriate way to communicate it, these projects wouldn’t have been as successful."
Karen Richardson of Redlands, California, is a writer for Esri.
This month, GPS World is excited to welcome our new Professional OEM editor, Tony Murfin.
Tony Murfin
When Alan Cameron first asked me a year ago to write for GPS World, I was delighted to think that an old crow such as myself could be considered as a potential contributor to this dynamic publication. At the time I had to decline, but as circumstances change — and change they certainly have for me — I now have the opportunity to say something which may from time to time appear to be partially intelligent to you, the readership.
People may know me as a VP at NovAtel, where I spent the large part of the last 15 years creating and sustaining a business around Wide Area Augmentation System (WAAS) ground-network reference receivers. This was a delightful company to work for as it grew from a small group of GPS engineering fanatics back in 1992, through an entrepreneurial express train which fed on innovation and success after success, to the respected GPS worldwide presence it has now become.
I really enjoyed that ride. It took basic engineering all the way through commercial introduction and acceptance with a small, specialized market base, out into the real world and captured huge programs, partnered with major companies, and fielded thousands of GPS receivers in multiple applications — applications we would never have dreamed of in 1992.
My relationship with NovAtel changed in July last year, when I transitioned into a part-time business consultant working in Canada and the United States, largely driven by my desire to avoid any more harsh Calgary winters and take up part-time residence in Florida. I parted ways with NovAtel this year as we successfully completed transition of a large number of activities to the very capable NovAtel team now executing this business.
But my GPS experience didn’t start with NovAtel; I first grew into GPS at CMC Electronics (previously Canadian Marconi Company) in Montreal. Before GPS, there were other ground-based navigation systems, and I began work at CMC as a software engineer on Omega. This system was based on low-frequency signals from eight worldwide ground beacons, and provided around one-mile accuracy for airborne users. Just as GPS today is used to aid inertial systems, integrated Omega and INS enabled international, over-water flights through the 1970s,’80s, and ’90s before GPS became fully operational.
CMC was one of the very first companies to develop and certify GPS for civil airline use. A long-term relationship between Honeywell and CMC has seen several generations of certified GPS receivers integrated into Honeywell civil aircraft avionics, as well as a host of non-related retrofits that have enabled a multitude of civil aircraft to benefit from GPS en-route navigation. CMC has now developed, certified, and fielded WAAS-capable GPS landing/approach sensors, and also GPS sensors which are the core sensor in GPS ground-landing systems (GPS Ground-Based Augmentation Systems (GBAS), better known in the United States as Local Area Augmentation System (LAAS)).
You’ll notice I keep stressing “certified” as a descriptor of these GPS receivers. Developing and qualifying receivers for use on passenger-carrying civil aircraft requires stringent development and test processes. It’s a difficult undertaking that involved proving compliance with a host of safety standards to certification authorities in a number of countries. This is a science unto itself, which CMC has mastered. I can claim some credit for their capabilities as I was also the software manager at CMC who introduced and coached the organization into initial compliance processes with these software standards in the 1980s and ’90s.
CMC also spun off a commercial low-cost OEM receiver family from its airborne receiver technology, and I was the first product manager who oversaw initial development and took this receiver to market.
Coincidentally, this product line was later purchased by NovAtel, and was the basis from which the current lower-end single-frequency NovAtel OEM receiver family has been derived.
While my consulting business may now take me into other aerospace technologies and products and systems, I’m still firmly grounded in GPS and GNSS. I expect future articles will deal with novel helicopter applications of GPS, news on Galileo and the other developing international satellite navigation systems, and how people are using these systems, both in the civil and military worlds.
You might have heard reports this week about a solar storm this week. This is part of the new solar cycle (Solar Cycle 24) that I’ve written about several times. I want to periodically touch on this subject as the solar activity is going to increase over the next few years, and if the solar activity (geomagnetic storms, not sunspots) is severe enough, it will have an effect on GPS accuracy and tracking. Here’s the scoop on this week’s solar activity.
First of all, I’ll let you in on a secret. I’m working on a new solar activity notification system specifically designed for GPS users. The problem is that people see reports in the mainstream media about solar activity and they automatically assume that it’s going to affect their GPS. Not all solar storms affect GPS; in fact only very specific ones (geomagnetic storms) of sufficient strength will affect GPS operations. I’m working on a notification system that will be tailored to both GPS L1 and GPS L1/L2 users (they are affected differently) so GPS users can have a reliable and specific source of information on solar activity without having to wade through the mainstream media noise.
Stay tuned for details this fall in this newsletter to learn more about my notification system and how to and access it. If you’ve ever used some of the GPS hardware/software products I helped design, you know my top priority is to make it easy to use and understandable.
This week’s event was probably the strongest geomagnetic storm of this solar cycle and of recent years (edit: actually, the storm in early April 2010 was a little stronger), maybe since late 2006. It will create some beautiful “northern lights,” but as strong as that may seem, it still wasn’t strong enough to elicit even a “cautionary” warning to GPS users (neither GPS L1 nor GPS L1/L2).
NASA video of sun’s activity on August 2, 2010
The last geomagnetic storm that adversely affected GPS users was in December 2006. It affected some GPS users for 10-15 minutes. For such a short time, most users would not notice or they might attribute it to a local system malfunction. By the time they investigate and reset the system, the event has passed and the user is back in operation. It was barely noticeable, if at all.
On the other hand, a severe geomagnetic storm such as the one that occurred in October 2003 can last for days and wreak havoc on precision GPS receivers. During extreme geomagnetic storms like that one, GPS accuracy suffers a lot, especially with GPS L1 users. During that event, simulations from the University of Calgary showed that WAAS maximum horizontal error (95th percentile) reached 25 meters while single baseline DGPS maximum horizontal error (95th percentile) reached 18 meters.
Dual-frequency users aren’t affected as much by extreme events but aren’t immune. Extreme events such as October 2003 can cause a loss of phase lock, especially with L2 on receivers that utilizing codeless and semicodeless techniques, which are virtually all of the dual-frequency GPS receivers on the market as of today.
For GPS users, nothing can be done to mitigate the effects of a strong geomagnetic storm. The next best step is to try to predict when they will occur so GPS users know what to expect. Fortunately, these storms are not common and scientists can reasonably predict when an event will occur.
There are some good websites to reference when checking up on solar activity. A great place for Europeans to do this is at the Royal Meteorological Institute of Belgium’s website. The U.S. National Weather Service also operates the Space Weather Prediction Center. The Australian Space Weather Agency operates a Space Weather Prediction Center, too. Also, note that for those users along the equator and at higher latitudes, your area is more susceptible to stronger geomagnetic storm activity.
The websites listed above are chock full of information and predictive systems on space weather. In fact, I believe it’s too much information for most GPS users to efficiently interpret. The goal with my new initiative is to provide GPS users with a quick summary so they are able to make informed decisions in a few seconds. Again, stay tuned this fall for the rollout.
Conference/Webinar Presentations
Between webinars and conferences, I’ve put together a fair number of Powerpoint presentations. I’m in the process of uploading many of them, some dating back years, to our website. Currently, I’ve uploaded ones that date back to April 2010. I hope you enjoy them.
The following presentations have all been converted to PDF format and are copyrighted. Feel free to incorporate them (or parts of them) into your documents if you like, just please remember to attribute each page you use to my name, Eric Gakstatter, and GPS World/Geospatial Solutions.
2010 (July San Diego, California) ESRI Surveying and Engineering GIS Summit luncheon keynote presentation: Get It Surveyed (GIS).
2010 (June, Seattle, Washington) Asia-Pacific Economic Cooperation meeting: Mapping and Surveying with SBAS+GPS.
2010 (June, Portland, Oregon) Webinar: GIS Mapping for Forestry, Agriculture, and Other Natural Resource Professionals.
Note that for the following webinar, you can also download an audio portion of the webinar free of charge by clicking here.
2010 (April, Phoenix, Arizona) GITA Annual Conference: How the Evolution of GPS is Transforming Surveying and Mapping (along with Pamela Fromhertz of NGS).
Part 1 – GNSS Mapping/Surveying Technology Update
2010 (April, Phoenix, Arizona) GITA Annual Conference: How the Evolution of GPS is Transforming Surveying and Mapping (along with Pamela Fromhertz of NGS).
Part 2 – Machine Control Using GNSS
2010 (April, Phoenix, Arizona) GITA Annual Conference: How the Evolution of GPS is Transforming Surveying and Mapping (along with Pamela Fromhertz of NGS).
Part 3 – Sub-Meter Mapping Using GPS
2010 (April, Phoenix, Arizona) GITA Annual Conference: How the Evolution of GPS is Transforming Surveying and Mapping (along with Pamela Fromhertz of NGS).
Question: How has your product and services mix changed, with the evolution of GNSS technology and users, since 1990 (or since your company was founded, or entered the GNSS market)?
Hemisphere GPS replies:
Like GPS World, Hemisphere GPS is proud to be celebrating our 20th anniversary in 2010. Over the past 20 years, our products have evolved, and continue to evolve, from a focus on providing positioning hardware to providing complete machine-control solutions as well as related services and applications. The evolution of GNSS technology has allowed us to create a more sophisticated and more accurate product line. We have been fortunate over this period to expand our market share in a variety of new industries. As GNSS technology matures, we are expanding our sales globally by servicing existing markets and finding new markets for our products.
Spirent Federal replies:
Spirent’s first simulator contracts were for GPS L1/L2 systems. During the 1990s, most customers were interested in these two GPS frequencies, often including classified P(Y) code simulation capability. GPS modernization is a major change that continues to shape the industry today. Spirent was first to launch GPS L2C, GPS L5, and M-code test systems into the market and developed SAASM-capable simulation systems for Precise Positioning Service (PPS) receiver testing. Growing concerns about RF interference and anti-jamming have led to Spirent GPS/inertial test interfaces and the development of CRPA test systems for comprehensive wavefront testing.
To enable testing of consumer GPS, Spirent developed a range of GPS L1 C/A code simulators which went on to sell widely to a whole new group of customers. Spirent delivered GPS plus GLONASS simulation during the 1990s. Today, with a nearly full GLONASS constellation and confidence building in Galileo again, many companies are looking to improve performance through multi-GNSS-capable receivers.
Rakon replies:
Rakon started supplying the GPS market back in 1990 with 1 ppm TCXOs that were about 11.7 2 18.3 mm in size. At the time they were the smallest on the market, hand assembled, and orders were for 100,000 units per year. These larger discrete products sold between US$30- $50 per unit. In 2002 Rakon introduced the first 0.5 ppm TCXO in a 5 2 3.2 mm surface-mount package, and since then the market for PND and mobile phones has really taken off. Today the market is 100s of millions of units a year — and this is still growing fast. The products are down to 2 2 1.6 mm in size, five times the performance and a fraction of the cost they were back in 1990 (now under US$1 each).
At Rakon we’ve realized that GPS needs more than just headline frequency stability and have built an entire bespoke manufacturing process that targets the parameters that GPS is sensitive to. The mobile phone environment GPS needs to operate in today is extremely challenging. Rakon has been developing new designs in high-stability TCXO technology, to continue to develop cost-reducing solutions with unmatched performance.
Special Section Sponsors
Sponsors of this special section commemorating the 20th anniversary of GPS World publication also include CAST Navigation and ITT. The magazine thanks all advertisers over the years for their support in relaying the latest technical, system, and business news to the marketplace. GPS World reaches 133,152 core buyers across the GPS World brand: print magazine, e-mail newsletters, website, webinars, and social media.
Covers from 2002, 2004, and 2008.
Two Decades of Innovation
Question: What is the most significant innovation your company has made over the last 20 years, and how does it relate to a development in GNSS technology or market?
Rakon replies:
Rakon was the first to develop the smallest 1 ppm TCXO in 1990 and led the way again in 2002 with the first 0.5 ppm TCXO. Rakon convinced the GPS chipset companies on the advantages of this level of stability while still remaining cost competitive. Today 0.5 ppm is now industry standard.
Spirent Federal replies:
Spirent has always been engaged in research and development to meet the growing user demand and provide new solutions for the latest requirements. The last five years alone have seen many significant innovations. In 2006, Spirent was awarded a contract to support the in orbit validation phase of the Galileo project. Test signals were needed to exercise the receivers for the Galileo Ground Sensor Stations and the initial “Test User Segment” receivers. Spirent developed Galileo simulators that could accurately simulate GPS with Galileo in a wide range of conditions, including error states.
In 2007, Spirent Federal won a contract to supply SDS M-code simulation systems to Rockwell Collins in support of its MUE contract with the GPS Wing. In 2008, for NASA’s Orion project, Honeywell selected a Spirent GPS/inertial simulator to emulate inertial sensor output while concurrently simulating GPS RF signals. Additionally, Spirent brought the first GPS/GLONASS/Galileo/QZSS simulator to the market and developed a CRPA test system recently selected by Rockwell Collins for comprehensive wavefront testing.
Hemisphere GPS replies:
In 2000, we launched the Outback S guidance system for agriculture. Outback S provided farmers visual guidance through a light-bar style system. At the time, GPS guidance in agriculture was in the infancy stage and due to its high cost was only accessible to a small number of users. Outback S brought GPS-based guidance to the agriculture market at a new price point and with a simple, intuitive user experience that appealed to the mainstream farmer. By the end of 2001, Outback was the number-one selling GPS guidance system for agriculture. We have since expanded on this innovation to include affordable auto-steering and continue to take pride in being “The leader in performance and value.” Today, we continue this value for performance legacy with our newest product, Outback eDriveX, which provides the highest accuracy steering available in the market at very compelling value.
Two Decades of Eager Users
Question: How have your customers/users developed or adapted over the last 20 years, as GNSS technology has developed? Or, have you changed what customers/users you sell to?
Spirent Federal replies:
Traditional users of GPS have developed to take advantage of new opportunities offered by improved and new signals, evolving technology, and research findings. The focus has shifted from getting receivers to navigate, to improving performance, systems integration, and user experience. Resilience has been a key focus for many users, who want to have not only high availability but also position information that they can trust.
In 1990 there were very few users of GNSS. Today GNSS is close to “the fifth utility,” with near ubiquitous deployment in vehicles in some countries and also increasingly in mobile phones. GNSS is used in many ways, including in innovative and unforeseen applications. Just one example is the possible use of GNSS to determine driving dynamics so that insurance premiums for more careful drivers can be set lowest!
Rakon replies:
Initially Rakon’s customers were involved mainly in marine, military, surveying, and agriculture. GNSS is increasingly becoming part of our modern-day infrastructure and services. Positioning capability is constantly being designed into an extending range of mass marketed consumer applications. Today we have many PND customers and those making products with GPS capability such as in mobile and smartphones and telecommunications. Customers have disappeared and many have changed significantly as the market has evolved; however, a core group has been with us since they started.
Hemisphere GPS replies:
In the past, the majority of our customers and users were very technically sophisticated. They were often educated in the field and demanded products solely based on position accuracy. Over time, our users have come to demand much more from our products and GNSS technology in general. Advancements in technology have also created a new category of customer who may be less technically sophisticated with the technology but who are looking for simplified solutions to complex problems. This has led us to focus our product development on more complete solutions that meet specific applications.
About This Magazine
Question: In your view, how has GPS World changed to reflect developments in the marketplace, the technology, customers’ needs, and your marketing needs?
Spirent Federal replies:
GPS World has been a valued companion for those involved in GNSS technology development. Many in the industry are deeply involved in a particular aspect of GNSS technology and find the broad, accessible perspective offered by GPS World very valuable. Many will remember reading about new signal performance first in GPS World — the first GIOVE Galileo signals from space and new Compass signals, for example. Key themes have also included vulnerability of the GNSS signals, from the Volpe Report through to analysis of the recent SVN-49 issues.
Hemisphere GPS replies:
GPS World has done a fantastic job in highlighting the evolution from GNSS technology to the myriad of both consumer and industrial applications the technology now enables. The publications are timely and consistently produce a credible resource for industry professionals. From a marketing perspective, GPS World’s expansion into online media has broadened its scope and circulation.
Rakon replies:
Originally the publication focused on the U.S. Global Positioning System. With the advent of others such as Galileo, GLONASS, and Compass, the publication has evolved to cover all GNSS systems.
