3.1415926…. π. Every nerd’s favorite number. It is the ratio of a circle’s circumference to its diameter in conventional or Euclidean space. We use it, for example, to convert angles measured in radians to degrees (π radians = 180 degrees). π is an irrational number, which means that its value cannot be expressed exactly as a fraction m/n, where m and n are integers. Consequently, its decimal representation never ends or repeats. But we sometimes use an easily remembered fraction, such as 22/7, to get an approximate value. In this case, 3.14. But, if we compute more digits with this fraction, we get 3.1428571…, clearly an incorrect result. A better way to remember π to eight digits is to count the number of letters in each word of the mnemonic “May I have a large container of coffee?”
In computations related to GPS, how many digits of π should be used? It depends. If you are developing your own algorithms and software for modeling GPS observations or determining precise orbits for the satellites, you’ll likely need π to 16 digits for double-precision floating-point calculations. But it would be a mistake to use π to this precision in computing the position of a satellite from the broadcast ephemeris. The GPS interface specification document, IS-GPS-200, specifies a 14-digit value for π (3.1415926535898) in the satellite coordinate computation. Use fewer or more digits, and the resulting satellite coordinates will not be as accurate.
An electronic pelorus is poised to become a useful tool in any mariner’s toolbox of resilient PNT systems. Learn how it works, and the benefits it brings to position fixing at sea.
INNOVATION INSIGHTS by Richard Langley
POP QUIZ: What do a character from Greek mythology, a point on the coast of Sicily, the pilot of Hannibal’s ship, a fizzy wine from New Zealand, and a navigation instrument have in common?
They are all called Pelorus or pelorus in the case of the instrument as it’s not a proper noun (grammar lesson over). And while a discussion of each of the uses of the word could be quite educational, this month’s column, perhaps predictably, will be about the pelorus or rather a modernized version of it.
If you are a landlubber, like me, you may not have heard of the pelorus. Yet, in one form or another it has been around for hundreds of years although not always going by that name. In appearance and use, it resembles a compass with sighting vanes.
But it has no magnetic components of any sort. And while a compass is used to get a magnetic bearing of a charted feature such as a tower or lighthouse or the magnetic heading of a vessel, a pelorus is used to measure a relative bearing between a feature and a reference direction such as the heading of the vessel, commonly called the ship’s head.
If a line is drawn on a chart through the sighted feature at an angle equal to the measured bearing, the vessel must be somewhere along this so-called line of position. If a second bearing on another feature significantly displaced from the first is measured in quick succession, a second line of position can be drawn on the chart, crossing the first.
The intersection point gives the (two-dimensional or horizontal) location, or position fix, of the vessel. Since the measured bearings will have some error, generally at least three lines of position are established with their intersections forming a small triangle, sometimes called a “cocked hat.” The location of the vessel is either inside the triangle or nearby depending on the similarity of the bearing errors.
Position fixes can also be obtained from instruments that measure ranges. In this case, the lines of position are circles for terrestrial systems providing two-dimensional fixes or spheres of position in the case of three-dimensional fixes obtained from GNSS measurements.
But let’s get back to the pelorus. Most vessels of a certain size are equipped with a pelorus. Frequent use of the pelorus helps to maintain situational awareness and being a completely passive device, it is not dependent on receiving an electronic signal of any kind. Only an acceptable level of visibility is required. And it can provide a manual check on any automated ship’s systems such as a GNSS receiver.
However, determining position fixes using a pelorus and a paper chart is laborious and time consuming and it is cumbersome to manually add lines of position to an electronic chart. What is needed is an electronic pelorus, which measures bearings electronically and automatically generates a line of position on an electronic chart.
The General Lighthouse Authorities of the United Kingdom and Ireland, the agencies responsible for aids to navigation in the U.K. and Ireland, have developed such an instrument. Dubbed the BinoNav, it is poised to become a useful tool in any mariner’s toolbox of resilient PNT systems and in this month’s column, we learn about its genesis, how it works, and the benefits it brings to position fixing at sea.
The overreliance on GNSS is well known and widely publicized. While GNSS is generally available, concerns remain on how maritime operations, and safe navigation in particular, are affected should GNSS not be usable, or become denied for any reason.
The General Lighthouse Authorities of the United Kingdom and Ireland (GLA) have been working on resilient positioning, navigation and timing (PNT) for many years. This work has included a comprehensive review of different potential solutions and their availability. One option proposed is the development of a ship-based positioning system that makes use of a modernized pelorus to work with a modern bridge.
Pelorus systems work by providing bearings from fixed positions, normally on the vessel bridge wings, to specific targets visible to the mariner and identified on the navigation chart. By taking several bearings in quick succession, intersecting lines can be drawn on the navigation chart, providing a position estimation. Clearly, there are limitations to this approach — these are explored within this article, but can be summarized as:
Automation. The time taken to measure the bearings can limit the achieved accuracy.
Visibility. Performance is limited by the mariner’s ability to see unique targets.
Paperless bridges. Many vessel bridges are moving away from paper, limiting the mariner’s ability to take bearings and plot them.
e-Navigation. More bridge systems require electronic values of latitude and longitude.
In an attempt to resolve most of these limitations, the GLA has been working on the development of an enhanced pelorus, or ePelorus, with its name registered to the Research and Radionavigation Directorate (R&RNAV) as BinoNav.
Prototype BinoNav systems have been developed and installed on all GLA vessels for trial. They enable the navigator to take visual bearings to known targets, from anywhere on the bridge using a handheld device — they are no longer confined to the bridge wings and targeting port or starboard objects.
Measured bearings are automatically registered and drawn on an electronic chart. Multiple bearings can then be made with ease, each of which is displayed on the chart and the intersecting “cocked-hat” position (to be discussed later), calculated automatically. This information can then be used to feed other bridge systems and confirm the vessel’s position.
In this article, I will provide a comprehensive overview of the BinoNav system, provide the results of initial trials and explain the planned development of the proposed resilient PNT solution.
e-NAVIGATION
Much has been written about e-Navigation elsewhere, but briefly, it is the International Maritime Organization’s (IMO’s) concept for the future of navigation, instigated by the U.K. Department for Transport in 2004. It will lead to the integration of systems and data — for the exchange of relevant geolocated information — faster and more cost effectively, and it will do this in the context of larger, faster vessels operating in ever more constricting shipping lanes and increasing offshore obstacles such as renewable energy infrastructure as well as the legacy of non-renewable energy infrastructure.
e-Navigation is designed to enhance safety of life for the mariner, improve protection of the environment, and increase energy efficiency in terms of shorter routing for fuel-efficient shipping. Moreover, it will allow more effective use of resources and integration across transport modes, including the more effective provision of integrated port operations.
Since its inception in 2004, development and delivery of e-Navigation services has been slow. Even now, some 14 years later, only a few prototype projects have delivered anything like what was anticipated in the original e-Navigation vision. This sluggishness has been caused by minimal leadership and drive from the IMO.
Despite this, some initiatives have been successfully delivered on a local or regional basis. These initiatives have come largely through projects such as Accessibility for Shipping, Efficiency Advantages and Sustainability (ACCSEAS), Efficient Safe and Sustainable Traffic at Sea (EfficienSea) 1 & 2, Motorways and Electronic Navigation by Intelligence at Sea (MonaLisa) 1 & 2, and Sea Traffic Management (a MonaLisa project), all of which have been supported by funding from the European Union.
Resiliency in PNT has been identified by the IMO as a lead area in the delivery of e-Navigation, and all these projects have used resilient PNT as the basis of what they have delivered.
REQUIREMENT FOR RESILIENT PNT
FIGURE 1. Ships’ systems affected by GPS jamming. (Data: Author)
It is now well recognized that all GNSS are vulnerable to interference, whether these interferers are from natural causes such as space weather or from synthetic sources such as jamming or spoofing devices. GNSS receiving units and satellite failures also occur. There are many examples of each of these problems affecting GNSS worldwide.
Resilient PNT information is needed to ensure continuity of maritime operations and safe navigation — especially for e-Navigation, management of sea traffic, and autonomous vessels.
GPS jamming trials were conducted by GLA’s R&RNAV in 1994, 2008, 2009 and 2012. These trials showed the real-time vulnerability of maritime systems to jamming. They identified that many ships’ systems were affected by GPS jamming. However, some systems we did not expect to be affected actually were (see Figure 1). Devices such as the helicopter-deck stabilization system and the ship’s gyrocompass are good examples.
GLA Work on Resilient PNT. GLA, through R&RNAV, has conducted a program of work that has looked at the issues of GNSS vulnerability and what they can do about it through a series of studies. These have looked at a number of systems such as
enhanced Loran, absolute radar positioning (two different methods)
ranging mode or R-mode, which is the use of ranging signals from existing marine infrastructure (two different methods)
signals of opportunity (many methods)
hybrid systems
dead reckoning
inertial
other on-board systems.
The timeline for the introduction of some of these systems into operational use, as well as current and new GNSS, can be seen in Figure 2. This article deals with equipment that falls into the “other on-board systems” category.
FIGURE 2. Timeline for resilient PNT (GNSS and complementary systems). (Diagram: Author)
A DRIVER FOR OPTICAL NAVIGATION SYSTEMS
The need for new optical navigation systems has been driven by a number of marine incidents, one of which I will discuss in detail.
MV Tricolor Incident. On Dec. 14, 2002, in early morning thick fog, on its way from Zeebrugge to Southampton, the MV Tricolor, with a load of almost 3,000 BMW, Volvo and Saab cars, collided with a Bahamian-flagged container ship named Kariba, about 20 miles north of the French coast in the Dover Strait Traffic Separation Scheme.
Albeit damaged above the water line, the Kariba could continue, while the MV Tricolor remained wedged on her side in 30 meters of water in a busy area of navigation. No lives were lost and the crew were rescued by the Kariba and a tugboat. Nevertheless, approximately 2,862 cars and 77 units of cargo, consisting mainly of tractors and crane parts, could not be salvaged.
The shipping lane, being the busiest in the world, was marked by buoys and guarded by the French police vessel Glaive and HMS Anglesey, thereby warning other vessels of the MV Tricolor’s presence. Despite the marking and patrolling, only two days later a cargo ship, Nicola, followed by another vessel, Vicky (carrying 70,000 tonnes of highly flammable gas oil) collided with the wreck of the Tricolor, after failing to heed several French naval warnings. In between the two further collisions, more buoyage and patrol vessels were deployed. On Jan. 22, a third accident happened when a salvage tug knocked a safety valve off the Tricolor, resulting in a massive oil spill.
Besides the heavy economic losses, including the estimated operation cost of around £25M (roughly $40M), the incident caused massive marine pollution and environmental contamination by spilling large quantities of oil. The Royal Society for the Protection of Birds estimated more than 1,000 birds were found dead or damaged by oil spilled from Tricolor.
Why Did It Happen? The incident was blamed on declining professional standards among seafarers, which was leading to scores of near misses in the area every day. Indeed, Andrew Linnington of the National Union of Marine Aviation and Shipping Transport Officers is quoted as saying that ship owners had been cutting costs by reducing use of deep-sea pilots to guide vessels through the world’s most crowded shipping lanes. Ships were increasingly crewed by one trained officer and a few poorly paid sailors from parts of the developing world.
“We know of at least four cases in the past year of ships going the wrong way in shipping lanes against the flow of traffic,” Linnington said. “Complaints are made to the states where the ships are registered, but they are often small countries used as flags of convenience and don’t have the resources to take action.”
It is clear from the incident and the ensuing investigation that navigators were not looking out the window, despite various radio navigation warnings and other methods, not the least of which was deploying wreck-marking buoys and virtual aids to navigation.
A very good way of mitigating the failure of any navigation system is by using reversionary methods of navigation, like looking out the window! This was a big driver in the GLA development of the BinoNav.
WHAT IS BINONAV?
FIGURE 3. A pelorus. (Photo: Author)
BinoNav is an electronic pelorus. A pelorus is a device that is completely independent of any other system or electronic position fixing system (EPFS), and this is important for providing resiliency.
Pelorus. A standard pelorus (see Figure 3) is used to take relative (to the vessel’s head) bearings to charted objects in the vicinity. The navigator then draws a line on the relevant navigation chart through the charted object. It is clear now that the vessel lies somewhere on this “line of position” from the charted object. This process is then repeated several times using different charted objects, with a minimum of three iterations.
This process then creates a “cocked hat” (a triangle in the case of three lines of position) generated from the intersection of the lines. Accounting for systematic errors, the vessel should lie somewhere within this cocked hat (see Figure 4 for an example).
This process is laborious and time consuming, but it does have the advantage of getting the navigator to look at real features outside the vessel — not just a red line on an electronic chart that they follow without question.
FIGURE 4. An example of positioning using a pelorus. (Chart: Author)
What about Electronic Chart Display? Electronic Chart Display and Information Systems (ECDISs) are excellent, when used correctly, and have driven innovation in the shipping industry. However, they do have disadvantages: If you are using a pelorus, you cannot very easily draw on a screen. You can generate an electronic bearing line (EBL) on an ECDIS, but it is a very long, convoluted way of providing a position not derived from an EPFS, such as a GNSS fix.
Any system that needs to generate an EBL on an ECDIS needs to do it electronically. Moreover, it needs to do this without having to rely on GNSS for position or time to avoid the issues of GNSS vulnerability: it should be completely independent. It should also be able to carry out optical to electronic integration to ensure that the mariner is looking out the window. Another GLA requirement was that it should be relatively low cost to make and distribute to enable take up across all users. So the idea of BinoNav was born. BinoNav fulfills all these criteria easily, intuitively and quickly, updating the electronic position of the vessel. Furthermore, with its wireless connection, bearings can be taken anywhere on the bridge of a vessel.
BINONAV FEATURES
In this section, I will describe the BinoNav and how it is used.
FIGURE 5. The BinoNav configuration. (Photo: Author)
Easy to Use. BinoNav comprises two parts: the “Bino” unit, which is a modified pair of binoculars, and a “base” unit that performs the communication link between the Bino unit and the electronic chart. Pick up the Bino unit from the base unit (see Figure 5 for overall configuration of the BinoNav).
Line up the graticule inside the Bino unit with a charted feature of use, press either of the buttons to automatically generate a line on the displayed electronic chart, which is relative to the ship’s head. As with a standard pelorus, one needs at least another two of these EBLs to generate a cocked-hat position on the electronic chart. Using either the touch screen or the mouse, “hover” over the cocked hat to generate a triangle. Now, right click to drop a marker at the center of the cocked-hat position and delete all lines. Once the vessel has moved (and dictated by the operating environment at the time), this process can be repeated. When two or more of the markers have been dropped, a line is drawn between the marks, thereby showing a track on the chart.
Features. From the use of the BinoNav unit as described above, a track is produced on an electronic chart that is not derived from an EPFS. This is important as it shows the integration of visual navigation into e-Navigation, something which e-Navigation has tried to do from the very beginning, as described by Brian Wadsworth in his earliest vision of e-Navigation (see Further Reading).
Another feature of BinoNav is “radar mode” for charted feature recognition. This feature draws a continuously moving line on the display that points at the position relative to the ship’s head. This is useful for the recognition of charted features when in unfamiliar territory.
The BinoNav is very easy to install, with only a connection for power and a connection for a suitable National Marine Electronics Association (NMEA) protocol data feed for heading. Many of its electronic components are available off the shelf and are widely available commercially with bespoke printed circuit boards. Some modification to the binocular unit has been necessary, with the addition of a bespoke unit, which links to the base unit for both orientation measurement and power when the unit is docked. The binoculars are readily available for around $500. The gyros incorporated in both the base unit and the binocular unit are high-grade microelectromechanical systems (MEMS) devices giving an angular resolution of 0.25-0.5 degrees, similar to that of a standard pelorus.
Currently, the BinoNav is 3D-printed, which allows for the quick production of one-off units. However, this approach is clearly not a suitable solution for long production runs and would require a different method of production.
FIGURE 6. The BinoNav installation on THV Alert. (Photo: Author)
Something for the Future. R&RNAV has received a lot of interest in the BinoNav not only from our own mariners, but also from a variety of influencers in the maritime world. We have had a great deal of positive feedback on potential improvements and additional features that we plan to develop.
We will also seek to gain approvals through IMO and the International Electrotechnical Commission to integrate BinoNav with ECDIS, so there will be no need for separate displays (unless being used on non-SOLAS vessels; that is, ones to which the International Convention for the Safety of Life at Sea does not apply.)
CURRENT GLA INSTALLATIONS
FIGURE 7. Using the BinoNav on ILV Granuaile. (Photo: Author)
The BinoNav has been installed on all six GLA vessels: ILV (Irish Lights Vessel) Granuaile, NVL (Northern Lighthouse Vessel) Pharos, NVL Pole Star, THV (Trinity House Vessel) Alert, THV Galatea and THV Patricia. The installation on Alert is shown in Figure 6 and BinoNav use on Granuaile is shown in Figure 7.
CONCLUSIONS
The key points made in this article can be summarized as follows:
e-Navigation is based on the premise of electronic navigation from “berth to berth.”
Many accidents happen because crews do not look out the window.
There is a need for electronic positioning from non-GNSS sources.
The BinoNav integrates visual navigation and electronic navigation through an ECDIS.
The BinoNav provides an independent verification of position with or without EPFS.
INTELLECTUAL PROPERTY
BinoNav is a registered trade mark and carries unregistered design rights. BinoNav has patents pending.
ACKNOWLEDGMENTS
The author thanks the masters, officers and crews of all the GLA vessels for their help and for the benefit of their experience throughout the whole process of the BinoNav development. Special thanks go to those who helped during the various development trials on ILV Granuaile and THV Alert prior to the mainstream installations.
This article is based on the paper “BinoNav® – A New Positioning System for Maritime” presented at ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018.
MARTIN BRANSBY is the head of the Research and Radionavigation Directorate at the General Lighthouse Authorities of the UK and Ireland, stationed in Harwich, Essex. He is responsible for the delivery of its program portfolio in research and development in technically diverse areas such as resilient PNT, e-Navigation, GNSS, Automatic Identification System (AIS) and visual signaling. He is a fellow of the Royal Institute of Navigation, and holds memberships in the Institute of Engineering and Technology and The Institute of Navigation. He is also a member of the International Association of Marine Aids to Navigation and Lighthouse Authorities’ AtoN (Aid to Navigation) Requirements and Management Committee.
FURTHER READING
Author’s Conference Paper
“BinoNav® – A New Positioning System for Maritime” by M. Bransby in Proceedings of ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018, pp. 1728–1735.
“Marine eNavigation: An Orientation Paper” by B. Wadsworth, document WEND9-INF4, presented to the 9th meeting of the International Hydrographic Organization World-wide Electronic Navigational Chart Database (WEND) Committee, Monaco, April 7–8, 2005.
“The Impact of GPS Jamming on the Safety of Navigation” by S. Basker, A. Grant, P. Williams and N. Ward, presented at the 48th meeting of the Civil GPS Service Interface Committee, Savannah, Georgia, Sept. 15–16, 2008.
Multi-GNSS will open up PPP to a much wider range of applications.
By Francesco Basile, Terry Moore, Chris Hill, Gary McGraw and Andrew Johnson
INNOVATION INSIGHTS by Richard Langley
ARE WE THERE? In a multi-GNSS world, that is. We’ve asked that question from time to time in this column over the years. So, are we there yet? That depends. One definition of “multi” is more than one. In this sense, we were in a multi-GNSS world as long ago as 1996. In that year, we had two fully populated constellations of satellites: GPS and GLONASS. Unfortunately, the full GLONASS constellation was short-lived. Russia’s economic difficulties following the dissolution of the Soviet Union hurt GLONASS, and by 2002 the constellation had dropped to as few as seven satellites. But GLONASS was reborn, and by Dec. 8, 2011, a full 24-satellite constellation was again operational.
But another meaning of “multi” is many, implying more than two. In the late 1990s, the first satellites to host transponders for satellite-based augmentation systems were launched. So, by the mid-2000s, even though GLONASS was still undergoing its rejuvenation, we were already in a three-constellation world. And receivers then on the market provided the necessary raw measurement data to yield positioning solutions from this system of systems with potentially more continuity and greater accuracy than those obtained using GPS alone.
And so in July 2008, we featured the article “The Future is Now: GPS + GLONASS + SBAS = GNSS.” And then in June 2010, we had “GPS, GLONASS, and More: Multiple Constellation Processing in the International GNSS Service.” In the introduction to that article, we asked that same question: Are we there yet? We concluded that, for early adopters of GPS plus GLONASS data and products, we were. With Galileo test satellites in orbit and an early version of the BeiDou system operational, it was already clear that by the end of the current decade, it wouldn’t just be the early adopters who would be benefiting from multi-GNSS but virtually all users of satellite-based positioning and navigation.
Although we aren’t quite there with fully operational Galileo and BeiDou constellations, we are getting pretty close. And so researchers are looking hard at how to make the best use of multiple-constellation observations in a variety of positioning and navigation scenarios. In this month’s column, a team of such researchers examines the potential benefit of combining GPS and Galileo observations for improving precise point positioning in urban environments, following the advice we read in the Book of Ecclesiastes: “Two are better than one.”
Over the years, precise point positioning (PPP) has been applied to many real-time applications that require sub-decimeter-level accuracy over a wide area or on a global scale. It is currently a standard in scenarios characterized by open-sky conditions, where a receiver is likely to have continuous track of GNSS satellites. On the other hand, PPP’s typically long convergence time means the technique has not been widely used in constrained and transient signal environments associated with urban areas. Analysis with both simulated and real data has shown that, once Galileo reaches final operational status, the PPP convergence time will be cut by more than half when using both GPS and Galileo observations. Accordingly, multi-GNSS will open up PPP to a much wider range of applications.
To begin, we assessed the positioning performance of GPS and Galileo signals, alone or used together, in open-sky conditions. A Simulink-based software simulator was used to simulate 24-hour-long observation sessions from 10 static (fixed location) receivers spread worldwide, which were then processed with the POINT software (developed by the University of Nottingham and three other British universities) in static (receiver assumed fixed) PPP mode with an elevation cutoff angle of 10° and with carrier-phase ambiguities estimated as real or floating-point values. For each station, the simulator was run 55 times to provide a sufficient number of data points to characterize the general behavior of the processing algorithms; therefore, a total of 550 points were considered.
For better GPS-Galileo interoperability, PPP results based on the ionosphere-free (IF) combination between GPS L1 and L5 and Galileo E1 and E5a observables were considered.
The metrics used to define the positioning performance are the errors in the north, east and down components of the position once all of a daily file has been processed and the time these errors take to converge below 10 centimeters.
The open-sky condition always guarantees excellent geometry and signal continuity even considering only one constellation.
PPP Results. TABLE 1 shows the root mean square (RMS) of the errors and convergence times of the three components of position for the different configurations for the 550 points considered. Both single- and dual-constellation systems are able to provide a sub-decimeter-level accuracy after a few tens of minutes. On average, positioning with Galileo E1-E5a IF performs better that GPS L1-L5 IF: the Galileo solution is more accurate and converges faster than the GPS solution.
TABLE 1. Comparison between GPS-only, Galileo-only and GPS plus Galileo PPP results. RMS of the positioning errors and convergence times for the stations considered.
The reason for this behavior is the assumed lower noise on Galileo pseudoranges. It is well known that the quality of the pseudoranges affects the convergence time of the PPP solution.
For this reason, one would expect some improvements by employing the Galileo Alternative BOC (AltBOC) modulated E5 signal. Thanks to its very large signal bandwidth of at least 51 MHz, Galileo E5 is characterized by excellent rejection properties of both long-range and short-range multipath. However, as shown in Table 1, when comparing the PPP solutions obtained using the Galileo E1-E5 IF and E1-E5a IF combinations, they have nearly the same performance. The reason for this apparent contradiction can be found in the use of the IF combination with E1. Given that E1 represents the dominant source of error in the IF combinations, its noise is amplified by a factor of 2.34 in the IF combination with E5 and by a factor of 2.26 when combined with E5a. Also, the smaller errors (with respect to E1) in E5a are amplified by 1.26, while the one in E5 is amplified by 1.34. Therefore, depending on the noise level in the Galileo pseudoranges, there might be instances where the noise in the E1-E5 IF combination is close to the one in the E1-E5a IF combination.
The number and the geometry of the observed satellites also affect the convergence time. For this reason, when using the two systems together, the time the vertical errors take to go below 10 centimeters was reduced by 50 percent with respect to the GPS-only case and by 18 percent with respect to the Galileo-only case.
URBAN ENVIRONMENTS
The poor signal visibility and continuity associated with urban environments, together with the slow (re)convergence time of PPP, usually make the technique unsuitable for land navigation in cities. However, as demonstrated in the previous section, using a dual-constellation not only improves the visibility conditions, but also reduces the PPP convergence time. Therefore, it might be possible to extend the applicability of PPP to land navigation in certain urban areas.
To assess the positioning performance of two-constellation GNSS in these constrained environments, we analyzed the signal availability and geometry of five different simulated sites in the neighborhood of the University College London (UCL) campus. We adopted building boundaries, which determine the minimum elevation angles above which GNSS signals can be received due to building obstruction. FIGURES 1 and 2 illustrate the location and the building boundaries for each site. FIGURE 3 shows the junction (site B) between Gower Street (site A) and University Street (site C).
FIGURE 1. Locations of the urban sites that are considered in the analysis.FIGURE 2. Building obstruction masks controlling satellite visibility for each site.FIGURE 3. Google Map image showing the junction (site B) between Gower Street (site A) and University Street (site C) in the midst of the University College London main campus.
When processing data from multi-constellation GNSS, the differences between the system time of the different constellations need to be considered. For this reason, when GPS and Galileo are used simultaneously for precise positioning, the Kalman filter state vector (in general) includes the three position components, the receiver clock offset, and the GPS-Galileo Time Offset (GGTO) — whether or not a predicted value might be available in a navigation message from one of the constellations. On the other hand, in PPP processing, the multi-constellation precise products used are based on the same system time, and therefore, in theory, it is not necessary to estimate the GGTO. However, existing intersystem biases may affect the PPP performance, and so it is advisable to estimate them in the Kalman filter.
Traditionally in PPP, the state vector also includes the residual zenith wet tropospheric delay and the carrier-phase ambiguities. Therefore, the minimum number of satellites required for GPS plus Galileo PPP is six. The geometry conditions are also an important factor for assessing the GNSS positioning performance. For land navigation, the horizontal dilution of precision (HDOP), which provides information about the achievable horizontal precision (and, assuming a bias-free solution, accuracy), is particularly relevant. For many land applications, such as precision agriculture and urban positioning, horizontal accuracy is more critical than vertical accuracy. Assuming that the ranging error in the carrier phase is 20 centimeters, to have decimeter-level horizontal accuracy HDOP needs to be no larger than 5. In most cases, HDOP values as small as 2 are desired.
TABLE 2 gives an overview of the visibility and geometry conditions at the selected sites. A dual-constellation (GPS and Galileo) receiver placed at one of the two road junctions will always, or almost always, see at least six satellites with an HDOP better than 5. At sites A and C, these minimum requirements for signal availability and geometry are met for more than 75 percent of the day. Obstructions due to high buildings, such as at site E, allows us to have at least six satellites for only 13 percent of the time.
TABLE 2. Percentage of epochs in 24 hours for which dual-constellation GNSS meets the minimum visibility (number of satellites, N) and geometry requirements (horizontal dilution of precision, HDOP).
From our preliminary study, it seems clear that high-accuracy positioning in urban environments is possible, but only in some areas where buildings are relatively short, providing good signal availability and geometry. Things can slightly improve by considering additional systems, such as GLONASS and BeiDou, and by exploiting the non-line-of-sight (reflected) signals. However, it is well known that an additional obstacle for PPP in urban environments is signal discontinuity. Indeed, when a GNSS receiver loses lock on the carrier, the positioning filter needs to be reinitialized, meaning that further tens of minutes are required before reconvergence.
To test the reconvergence time of PPP in transient signal environments, a pedestrian carrying a multi-GNSS receiver was simulated to be walking along the path in FIGURE 4. The receiver was simulated to be located for the first half hour of the simulation in the front yard of UCL’s Wilkins Building (where the simulation begins and ends), before starting to move. This is to allow the initial convergence of the PPP filter.
FIGURE 4. The measured trajectory of the simulated pedestrian kinematic test.
FIGURE 5 shows the visibility for a given GNSS satellite. Only the epochs when the receiver is moving are considered. Therefore, the first 30 minutes, when the receiver is static, are not included in the plot. Data gaps due to building obstructions are visible, with the largest being about 12 minutes and the average less than 2 minutes. As a consequence, the carrier-phase ambiguities need to be estimated all over again; and, as previously mentioned, this process usually requires tens of minutes before reconvergence.
FIGURE 5. Satellite availability during the kinematic test.
FIGURE 6 shows the HDOP and the number of visible satellites for the kinematic test, while FIGURE 7 shows the RMS, over 50 simulations, of the horizontal components of the positioning error when GPS L1 and L2 and Galileo E1 and E5, linearly combined into the IF combination, are processed in kinematic PPP mode with the POINT software. At the beginning of the kinematic test, when the HDOP is well below 5, the horizontal error is at the centimeter level, while, after 33 minutes from the beginning of the simulation, building obstructions don’t permit a converged solution below the 20-centimeter accuracy level.
FIGURE 6. Horizontal dilution of precision and number of visible satellites for the kinematic test.FIGURE 7. RMS of the position errors for the kinematic test.
This short example clearly demonstrates that two-constellation PPP has, in theory, the potential to precisely navigate ground vehicles in some urban environments; however, it is too sensitive to signal discontinuity. Slow solution reconvergence to the few decimeter/centimeter level still represents the main limitation to the use of PPP for high-accuracy applications in cities. Nonetheless, GPS plus Galileo PPP easily enables sub-meter-level horizontal accuracy for most of the simulations we have carried out. After signal loss, it only took a few tens of seconds to have a horizontal accuracy of better than a meter.
SMOOTHED CORRECTIONS
As an alternative to ambiguity-fixing methods aimed to improve the (re)convergence time, we propose a method that mitigates the effect of the ionosphere and which thereby reduces the reconvergence time of the PPP solution after initial convergence has been achieved. In this new approach, while the two-frequency carrier phases are linearly combined in the traditional IF combination, the uncombined pseudoranges are corrected by a pre-smoothed ionospheric delay (via a Hatch filter), computed using the geometry-free combination of two-frequency pseudoranges.
Once the Hatch filter has converged, ideally we have IF pseudoranges with lower noise than the traditional ones. Therefore, in case the PPP filter needs to restart, we can obtain a quicker reconvergence thanks to the lower noise on the ionosphere-corrected pseudoranges. Indeed, provided that the signal gap is not very large, the ionosphere smoothing filter doesn’t need to be restarted from the raw values.
It is possible to predict the ionospheric delay computed from two-frequency carrier-phase measurements using a linear fitting model from previous measurements within a sliding time window. As an example, high-rate data recorded on July 25, 2017, from station DAEJ in Daejeon, Republic of Korea, were used to analyze the ionosphere prediction error.
In FIGURES 8 and 9, the RMS of the prediction errors for different time windows have been plotted against the data gap length. The prediction error depends on both the time latency of the observation and the elevation angle of the satellite. It increases with the data gap length, but larger time windows can damp the divergence of the error. A time window of 120 seconds was used both for satellites above and below 30° elevation angle. In this case, the error for a 5-minute prediction is about 4 centimeters for a satellite above 30° and 7 centimeters for satellites with a low elevation angle. These values are much smaller than the noise in the pseudorange measurements and can, therefore, be neglected.
FIGURE 8. RMS of the prediction errors vs. data gap length for satellite elevation angles greater than 30°.FIGURE 9. RMS of the prediction errors vs. data gap length for satellite elevation angles less than than 30°.
Multi-Frequency Combinations. The method introduced in the previous section allows users to be free from the constraint of IF observables and, therefore, to look for multi-frequency combinations aimed to minimize the noise on the pseudoranges. The next-generation GNSS satellites will broadcast open signals over three frequencies. The triple-frequency, geometry-preserving combination aimed to reduce the noise, instead of mitigating the ionosphere, can be used for positioning purposes.
TABLE 3 summarizes the assumed values for the ratios ni between the noise on different GPS and Galileo pseudoranges and the ones on L1/ E1. FIGURE 10 shows a color map of the noise amplification factor associated with different linear combinations between GPS L1, L2 and L5. The x-axis is α3, the coefficient multiplying the pseudorange on L5 in the combination, while the y-axis is the ionosphere amplification factor of the triple-frequency combination with respect to L1, q. The noise for this combination can be as little as 0.57 times the noise on L1, while the corresponding ionosphere amplification factor is 1.49. Once the smoothed ionosphere correction has converged, we can potentially have an IF pseudorange 81 percent less noisy than the L1-L2 IF, and, therefore, a much faster reconvergence.
TABLE 3. Assumed noise, ni, on GPS and Galileo pseudoranges, i, and their ionospheric delay, q, with respect to L1/ E1.FIGURE 10. Geometry-preserving surface in the space q-α3-n (ionosphere amplification factor – L5 pseudorange multiplier – noise amplification factor) for GPS L1-L2-L5 combinations.
Similar conclusions can be drawn by considering Galileo signals. Using triple-frequency combinations with E1, E5a and E5b, we can obtain 81 percent less noise than E1-E5a IF, while a reduction of the noise in the IF pseudorange up to 90 percent was observed using E5 alone. Triple-frequency combinations involving E5 don’t bring such large improvements with respect to using E5 alone. Indeed, a maximum of 16 percent less noise can be registered when combining E1, E5a and E5 with respect to the E5 uncombined case. TABLE 4 illustrates the minimum noise amplification factor for each triple-frequency combination and its ionosphere amplification factor.
TABLE 4. Minimum noise achievable through GPS and Galileo triple-frequency pseudorange combinations and their ionospheric delay with respect to L1/ E1.
The noise associated with the ionosphere-corrected multi-frequency pseudorange combination is as large as meter level before converging to centimeter level. For this reason, a proper weighting method, which considers the varying noise on the ionosphere correction, needs to be employed.
To test the benefit of the new approach for the reconvergence time, three hours of simulated GPS and Galileo data from a static site in La Misere, Seychelles, were processed with the POINT software in kinematic mode. After 90 minutes, the PPP filter was forced to restart to simulate reconvergence. The multipath time constant was set to 5 seconds, which is a typical value for kinematic multipath. The performance of the traditional L1- L2 IF combination was compared with the triple-frequency pseudorange combination, corrected by the smoothed ionosphere delay coming from the Hatch filter.
FIGURE 11 shows the precision (RMS error over 50 simulations) of the horizontal components after filter restart. The new approach has much faster reconvergence than the traditional PPP method based on the IF combination. Indeed, while the traditional method takes about 11 minutes to have a horizontal error below 10 centimeters, using the low-noise combination, this accuracy is achieved after 171 seconds. Even better performance can be achieved considering the Galileo E5 signal (see FIGURE 12).
FIGURE 11. RMS error of the horizontal position components of static site using GPS data after filter restart.FIGURE 12. RMS error of the horizontal position components of static site using Galileo data after filter restart.
The E1-E5 IF combination requires 10 minutes for the horizontal convergence, while using E5 with the Hatch filter we have the horizontal solution converged in about 30 seconds. It is worth noticing that in the presence of static multipath, the proposed weighting method may lead to an overly optimistic weighting of the pseudorange measurements in the PPP filter and to a slower reconvergence of the positioning solution. Indeed, the long correlation time in the static multipath, of the order of a few minutes, makes it hard to filter out by the Hatch filter, hence the corrected measurements have larger errors than expected.
The effect of static multipath in the new configuration is visible in FIGURE 13, where the reconvergence of the horizontal component for the L1-L2 IF combination is compared with the new approach. In this case, the time constant of the simulated multipath was set to 1 minute. In this scenario, the triple-frequency low-noise combination corrected by the smoothed ionosphere combination quickly converges below 20 centimeters; however, it takes significantly longer than the L1-L2 IF combination to reach the 10-centimeter accuracy level.
FIGURE 13. RMS error of horizontal position component of static site using GPS data after filter restart with 1-minute multipath time constant.
Also, the new method was tested with the kinematic simulation as in the previous section. Here, the GPS triple-frequency combined pseudorange and Galileo E5 pseudorange (both corrected with the smoothed ionosphere) are processed in kinematic PPP mode with the POINT software. FIGURE 14 compares the RMS of the horizontal errors with the IF configuration. Less than a minute after the receiver lost lock on the satellites, the solution reconverged below the 20-centimeter level, while it took less than 30 seconds to go below 50 centimeters.
FIGURE 14. RMS error of the horizontal position components of kinematic trajectory using GPS and Galileo data and the smoothed ionosphere approach after filter restart.
CONCLUSIONS
In this article, we described a comparison that we carried out between GPS-only, Galileo-only and GPS plus Galileo PPP. Results based on simulated open-sky conditions demonstrated that Galileo performs better than GPS thanks to an assumed lower E1-E5a IF noise with respect to L1-L5. Two-constellation PPP enables faster (re)convergence compared to the single constellation case.
An analysis of GNSS signal availability, continuity and satellite geometry was also performed to study the feasibility of PPP in urban environments. Preliminary results, based on simulations, showed that dual-constellation (GPS plus Galileo) PPP is possible in urban areas with relatively short buildings in which a satellite minimum availability requirement is met most of the time. However, signal discontinuity still represents the major problem for traditional PPP in urban environments, due to long reconvergence times.
Finally, we proposed a new PPP configuration based on triple-frequency combinations, intended to minimize the noise on the pseudorange and corrected by a smoothed ionospheric delay. This configuration seems to provide faster reconvergence than the traditional PPP with the IF combination if applied to kinematic scenarios. In static applications, the very slow varying multipath error makes the proposed weighting method, based on the error in the smoothed ionosphere correction, overly optimistic. In such cases, the IF combination reconverges more quickly to high-accuracy levels better than 20 centimeters.
ACKNOWLEDGMENTS
The research described in this article was sponsored through a studentship agreement between the University of Nottingham and Rockwell Collins UK Limited. The article is based on the paper “Multi-Frequency Precise Point Positioning Using GPS and Galileo Data with Smoothed Ionospheric Corrections” presented at the 2018 IEEE/ION Position, Location and Navigation Symposium, held in Monterey, California, April 23–26, 2018. All figures attributed to the authors unless otherwise specified.
FRANCESCO BASILE is a postgraduate research student at the Nottingham Geospatial Institute of the University of Nottingham in the United Kingdom. He received his M.Sc. in space and astronautic engineering from the University of Rome – La Sapienza and his B.Sc. in aerospace engineering from the University of Naples – Federico II, both in Italy.
TERRY MOORE is the director of the Nottingham Geospatial Institute where he is the Professor of Satellite Navigation. He is a fellow and the president of the Royal Institute of Navigation (RIN) and also a fellow and a member of council of the Institute of Navigation (ION).
CHRIS HILL is an associate professor in the Faculty of Engineering at the University of Nottingham and a member of the Nottingham Geospatial Institute research group. He holds a Ph.D. in satellite laser ranging and he is a fellow of the RIN.
GARY MCGRAW is a technical fellow with the Rockwell Collins Advanced Technology Center in Cedar Rapids, Iowa. McGraw is a fellow of the ION and is a senior member of the IEEE.
ANDREW JOHNSON is a chief engineer at Rockwell Collions UK in Winnersh, Berkshire, United Kingdom. Johnson has a B.Sc. in electronic and electrical engineering from the University of Surrey in Guildford, United Kingdom.
FURTHER READING
Authors’ Conference Paper
“Multi-Frequency Precise Point Positioning Using GPS and Galileo Data with Smoothed Ionospheric Corrections” by F. Basile, T. Moore, C. Hill, G. McGraw and A. Johnson in Proceedings of PLANS 2018, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Monterey, California, April 23–26, 2018, pp. 1388–1398, doi: 10.1109/PLANS.2018.8373531.
“Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models” by L. Wang, P.D. Groves and M.K. Ziebart in Journal of Navigation, Vol. 65, No. 3, July 2012, pp. 459–476, doi: 10.1017/S0373463312000082.
“Potential Benefits of GPS/GLONASS/GALILEO Integration in an Urban Canyon – Hong Kong” by S. Ji, W. Chen, X. Ding, Y. Chen, C. Zhao and C. Hu in Journal of Navigation, Vol. 63, No. 4, October 2010, pp. 681–693, doi: 10.1017/S0373463310000081.
Multi-Constellation Use in Aviation Applications
“Assessment of Alternative Positioning Solution Architectures for Dual Frequency Multi-Constellation GNSS/SBAS” by G. McGraw, B.A. Schnaufer, P.Y. Hwang and M.J. Armatys in Proceedings of ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 16–20, 2013, pp. 223–232.
“Undifferenced GPS Ambiguity Resolution Using the Decoupled Clock Model and Ambiguity Datum Fixing” by P. Collins, S. Bisnath, F. Lahaye, and P. Héroux in Navigation, Vol. 57, No. 2, Summer 2010, pp. 123–135, doi: 10.1002/j.2161-4296.2010.tb01772.x.
“Integer Ambiguity Resolution on Undifferenced GPS Phase Measurements and Its Application to PPP and Satellite Precise Orbit Determination” by D. Laurichesse, F. Mercier, J.-P. Berthias, P. Broca and L. Cerri in Navigation, Vol. 56, No. 2, Summer 2009, pp. 135–149, doi: 10.1002/j.2161-4296.2009.tb01750.x.
Hatch Filter
“Combinations of Observations” by A. Hauschild, Chapter 20 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
“The Synergism of GPS Code and Carrier Measurements” by R. Hatch in Proceedings of the Third International Geodetic Symposium on Satellite Doppler Positioning, Las Cruces, New Mexico, Feb. 8–12, 1982, Vol. II, pp. 1213–1232.
Dilution of Precision
“Dilution of Precision” by R.B. Langley in GPS World, Vol. 10, No. 5, May 1999, pp. 52–59.
Kalman Filtering
“Least-Squares Estimation and Kalman Filtering” by S. Verhagen and P.J.G. Teunissen, Chapter 22 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
UWB is being used in a novel microwave imaging and localization system, one which features Antonio Vivaldi’s namesake antenna.
By Fengzhou Wang and Guohua Wang
INNOVATION INSIGHTS with Richard Langley
VIVALDI. No, you aren’t reading an article in Gramophone. This happens to be the name of a particular kind of broadband antenna, which is particularly useful at microwave frequencies and for ultra-wideband (UWB) applications in particular. It was invented by the British electrical engineer Peter J. Gibson in 1978 while working at Philips Research Laboratories. In a 1979 conference paper entitled “The Vivaldi Aerial,” Gibson described it as “a new member of the class of aperiodic continuously scaled antenna structures and, as such, it has theoretically unlimited instantaneous frequency bandwidth.” He went on to say “This aerial has significant gain and linear polarisation and can be made to conform to a constant gain vs. frequency performance. One such design has been made with approximately 10 dBI gain and -20 dB sidelobe level over an instantaneous frequency bandwidth extending from below 2 GHz to above 40 GHz.” Broadband indeed!
So why did Gibson name the innovative antenna “the Vivaldi aerial”? It has to do with its shape. Another term for the Vivaldi antenna (sometimes called the Vivaldi notch antenna) is the tapered slot antenna. The planar antenna, constructed out of thin metal sheet or printed circuit board (PCB), features a slot line gap cut out of the sheet or etched from the PCB, which gradually flares in the direction of wave propagation (see Figure 1 in this month’s article to see what a Vivaldi antenna actually looks like). Since the spacing of the gap is related to the wavelength of the radio waves that can be launched, the antenna can be used over a wide frequency range not unlike the log-periodic antenna used in shortwave broadcasting or the biconical antenna and its butterfly antenna subtype used for UHF TV reception. Of course, according to the reciprocity theorem, an antenna designed to transmit radio waves can generally be used to receive radio waves with the same antenna properties (gain, bandwidth and so on).
But let’s get back to the tapered slot antenna’s formal name. According to one his co-workers, the shape of the antenna reminded Gibson (who was also a musician and composer) of the cross-section of an early trumpet. So he named his antenna after Antonio Vivaldi, the famous baroque music composer, who wrote several concertos featuring trumpets. And 1978, the year of the antenna’s invention, was the three-hundredth anniversary of Vivaldi’s birth. It doesn’t hurt that the shape of the slot also looks a bit like a cursive “V” when the antenna is stood on its end.
While the basic Vivaldi antenna generates (or receives) linearly polarized waves, it is possible to combine two elements at right angles to generate (or receive) circularly polarized waves.
Because of its broadband characteristics and ease of PCB manufacturing, the Vivaldi antenna has been used extensively in UWB applications. Conventional radio transmissions use a variety of modulation techniques but most involve varying the amplitude, frequency and/or phase of a sinusoidal carrier wave. But in the late 1960s, it was shown that one could generate a signal as a sequence of very short pulses, which results in the signal energy being spread over a large part of the radio spectrum. Initially called pulse radio, the technique has become known as impulse radio ultra-wideband or just ultra-wideband for short. The bandwidths of UWB signals are quite large. For example, in the U.S., current Federal Communications Commission rules for pulse-based positioning or localization implementations require the applied bandwidth to be between 3.1 and 10.6 GHz and the bandwidth to be greater than 500 MHz or the fractional bandwidth to be more than 0.2.
The use of large transmission bandwidths offers a number of benefits, including accurate ranging and that application in particular is being actively developed for positioning and navigation in environments that are challenging to GNSS such as indoors and built-up areas.
In this month’s column, we learn how UWB is being used in a novel microwave imaging and localization system, one which features Antonio Vivaldi’s namesake antenna.
Indoor localization is challenging work using traditional location-based services such as GPS. Approaches for indoor position estimation have used radio-frequency (RF) signals including narrowband signals such as Wi-Fi and Bluetooth. Impulse radio ultra-wideband (UWB) signals have also been widely investigated. Compared with narrowband signals, UWB signals provide high signal-to-noise ratio, which helps to provide an accurate estimate of signal arrival time for time-based location algorithms such as time of arrival (TOA). Furthermore, UWB signals provide larger coverage areas and a ranging capability. Sub-millimeter positioning accuracy is achievable. And UWB-based location has an inherent high time resolution making it useful in a tracking system for medical and other applications.
A number of investigations in UWB positioning have already been carried out, with several relatively expensive commercial UWB kits available from companies such DecaWave and BeSpoon. But additional work still needs to be carried out to fully evaluate the UWB solution, so this is still an open research topic. One problem area requiring further investigation is positioning in the non-line-of-sight (NLOS) environment. This is considered the main challenge for UWB location, since it is associated with strong fading due to reflection and diffraction from various obstructions such as furniture in the room. Various threshold crossing methods using techniques of energy detection, correlation and the multiple signal classification (MUSIC) spectral analysis algorithm have been used to resolve the multipath propagation problem in NLOS environments. However, these approaches require complicated signal processing, which increases the computing cost.
Moreover, UWB technology is also being widely introduced in microwave imaging for military and biological applications. It provides high-precision detection and high-resolution images, depending, in part, on the operating frequency range. The radar-based microwave imaging or MWI is a time-domain confocal imaging method that aims to indicate the position of the targets by use of the delay time of the reflected signal. MWI technology highlights the target from the testing environment by using different values of the dielectric permittivity constant.
In this article, we propose a hybrid method combining MWI and localization of body-worn UWB antennas for improving the accuracy of indoor positioning. The proposed system will be able to differentiate an LOS environment from an NLOS environment using MWI detection ability, and then adjust the scanning antenna array setup using robotic support. Furthermore, we introduce a threshold value in the filter function to highlight major obstructions in an NLOS environment such as a physical item. Using this proposed system for TOA measurements, we have obtained an overall average accuracy in two-dimensional localization of around 1.7 to 2.5 centimeters.
SYSTEM EXPERIMENTAL SETUP
We have developed a robotic antenna array for indoor microwave imaging to assist in indoor location with wearable antennas. The basic architecture of the proposed UWB localization system consists of two components: tag antennas and anchor antennas. Two thin-film tag antennas are worn on both shoulders of a human, and seven wideband Vivaldi antennas (also known as tapered slot antennas), acting as anchor antennas, are mounted on individual robotic supports, which can adjust the height and the rotation angle of each antenna. All the antennas are fabricated with printed-circuit board (PCB) material to reduce the cost.
FIGURE 1. UWB antennas setup for the proposed location approach.
In FIGURE 1, the Vivaldi antennas are shown with blue dots and are placed on the top of the robotic support 2 meters above the ground. The antenna array covers a scanning area with a radius of 2 meters. The two compact wearable tag antennas are placed on the left and right shoulders of the target human at a nominal height of 1.7 meters.
Other main components of the proposed system are shown in FIGURE 2.
FIGURE 2. The proposed system diagram.
The system can be manually controlled by an Apple iPad or automatically controlled by a personal computer (PC). The PC runs the National Instruments (NI) Laboratory Virtual Instrument Engineering Workbench (LabVIEW) programming environment and an NI instrument monitor for debugging the operating process. Further information processing is carried out by combining the received signal from a vector network analyzer (VNA) though the USB-based NI-DAQmx driver software and associated cable and a mobile device such as the Apple iPad for remote control and cloud access. Two ports of the VNA are connected to an RF switch to transmit and receive signals using the antennas located in the scanning area. During the detection phase, the anchor antennas are sequentially active, and a number of signal time series are transferred back to the PC for imaging processing. The delay-and-sum algorithm is used for signal processing and imaging reconstruction in Matlab to find the position of any obstruction in the scanning area.
The following specific components were used in the experimental setup shown in Figure 2: an Agilent HP 8510B VNA (operating from DC to 20 GHz for two-channel acquisition), a single-pole eight-throw (SP8T) switch (an Analog Devices HMC321LP4 on an evaluation PCB forming a switchboard), seven directional UWB Vivaldi receiving antennas (operating from 2 to 14 GHz); two body-worn UWB transmitting thin-film antennas (operating from 3 to 9 GHz), a reconfigurable input/output device based on a field-programmable gate array (FPGA) and a microprocessor (NI myRIO-1950 board), a general-purpose interface bus (GPIB) to USB cable (Agilent 82357B), and a personal computer running LabVIEW and Matlab.
PRINCIPLES OF OPERATION
In our proposed technique, the range-based TOA approach is implemented, making use of the high accuracy obtained by the fine time resolution of the applied UWB impulse signal. FIGURE 3 shows a flowchart of the proposed localization scheme in our approach. Initially, the system needs to be calibrated to normalize the responses of all the antennas in the anchor antenna array and to eliminate the effect of reflections from the environment. To calibrate the system for microwave imaging, no objects should be present in the scanning area at this stage.
FIGURE 3. Proposed scheme for UWB localization in realistic environments with multipath situations.
There are four main phases of the operation. Firstly, the radar-based UWB microwave imaging system is introduced into the localization system to classify the LOS and NLOS environments. If the environment is LOS, the system will go to the location phase directly. If the environment is NLOS, further operations for the antenna array configuration need to be carried out to reduce the multipath effect from the non-target object. In this case, the only located target is the pair of wearable tag antennas.
Secondly, the system moves to the imaging and classification phase involving the Vivaldi antenna array on the anchor station. Using UWB impulses for MWI, the imaging system can detect the existence of inhomogeneity within a structure or medium and a two-dimensional (2D) image can be developed as shown in FIGURE 4.
FIGURE 4. (Top) Layout of test setup. (Bottom left) The acquired imaging on shoulder plane before thresholding. (Bottom right) After thresholding.
During the imaging process, one wearable antenna is transmitting a Gaussian pulse while the other is receiving the scattered signals. Circular synthetic aperture radar (CSAR) and elevation-CSAR (E-CSAR) are widely used approaches to extract 2D spatial information of the imaging scenario and have been used for small area 2D remote sensing and foliage target detection. For our current work, we have adopted the CSAR approach. We developed Matlab code to process the data and generate images.
Various material obstructions such as hollow plasterboard boxes, solid concrete items and metal boxes were investigated during our experiments. We had to define threshold values for the various materials to get a more visually acceptable image.
According to the experiments, metal has a significant effect in NLOS environments, and the threshold value was used to optimize the final imaging result (a 20-pixel by 20-pixel image). The scanned area could be visualized with the imaging results depending, in part, on the heights of the antennas on the anchor station and the threshold value used. In this case, two hollow plasterboard boxes are filtered out, leaving the metal box in the image as shown in Figure 4(c).
In the third phase of the operation, the image result is fed into the machine learning algorithm used in the calibration phase. A pre-defined geometry of the antennas on the anchor stations, such as the six anchor stations in a cuboid shape, Y-shape or L-shape, was chosen for implementation in the current environment. The height and angle of the anchor antenna array pattern were adjusted using motors controlled by the NI MyRIO board. In this scenario, all the antennas on the anchor station are receivers (Rxs), and only body-worn antennas are transmitters (Txs).
In this particular experiment, the obstruction (the metal box) is detected on the right upper side of the scanning area, so the cuboid configuration was selected as the anchor station setup. Four antennas on the left of the area were selected as receiving antennas as shown in FIGURE 5. Figure 5(a) highlights one of the antennas.
FIGURE 5. (a) Setup of anchor station. (b) Pre-defined geometry setup for anchor stations used for the experiment of Figure 4.
Finally, in the fourth (location) phase, the time of arrival of the signal from the transmitting antenna array at the receiving wearable antenna is estimated by channel impulse response (CIR) and peak detection techniques. An inverse fast Fourier transform (IFFT) is then applied to obtain the impulse response of the measured channels. The channel impulse response is given by:
where δ is the Dirac delta function, K is the number of resolvable multipath components, τk are the delays of the multipath components, ak are the path amplitude values, and θk are the path phase values. The MyRIO board controls the RF switch to circulate each receiving antenna and the corresponding S-parameter value (S21) is passed through the GPIB-to-USB cable for storage in the personal computer. The CIR, a peak detection technique and a TOA data-fusion method are used to accurately estimate the target’s location (xm, ym). Let (xi, yi) represent the position of the ith transmitting antennas, and r represent the range value obtained from the TOA measurement:
RESULTS
Let us summarize the procedure we followed for an experimental test of our proposed approach as described in the previous section. Our hardware setup is shown in Figure 1, and we carried out the experiment to demonstrate performance in both LOS and NLOS environments. Firstly, a 2D image of the scene area was reconstructed using the time-varying backscattered intensities as shown in Figure 4.
Secondly, the image is processed based on a database to detect the dielectric constants of the obstructions. The shape of the obstruction might not be completely delineated as the low resolution of the image favors an increased efficiency of the imaging processing. However, the position of the obstruction can be found whether it is on a critical path or not. Thirdly, the proper archor-station setup is implemented using the MyRIO board to control the RF switch and antenna motors according to a pre-defined database in the personal computer. Lastly, the peak detection algorithm is used to estimate the TOA of the UWB signal from the Tx at the Rx. The TOA is directly estimated by the detection of the strongest peak of the CIR.
FIGURE 6 shows the localization results for the situation in Figure 4. The same experimental method was repeated but using a threshold-based TOA estimation procedure, and the results compared with our procedure. The results using that approach are also displayed in Figure 6.
FIGURE 6. UWB localization: estimated and actual positions of the antennas placed on the body for the environment as shown in Figure 5.
In TABLE 1, we summarize the localization errors obtained in the different environments using the two estimation techniques. The average accuracy achieved for our proposed approach for a single antenna is in the range of 3 to 5 centimeters. Given that there are two sensing antennas, one on each shoulder, we could establish a middle point as the position of the human body, and combining the results of each antenna, we could improve the accuracy to about 2.5 centimeters in the NLOS environment.
TABLE 1. Average localization error in centimeters for different testing environments with different estimation methods.
The method accuracy depends on the pre-defined solution for the anchor antenna array in the NLOS environment, and the estimation accuracy could be improved by training the hardware during the operating period. Furthermore, the localization accuracy also can be enhanced by increasing the number of active anchor stations. However, this will cost more in terms of hardware implementation and also require more space for the apparatus.
CONCLUSIONS
This article presents a hybrid UWB technique combining radar-based microwave imaging and localization of a body-worn UWB antenna for mapping 2D environments. We provided an overview of the concept and method of detecting obstructions, and described a sample implementation that proved the concept and provides ideas for further improvements.
Our results demonstrate the usefulness of the proposed technique, which provides similar performance regarding computational load and accuracy compared to traditional methods using a threshold-energy-based algorithm such as the search-back method and least-edge detection methods. The technique also is able to distinguish between LOS and NLOS environments.
Our approach has some advantages compared to the common methods for NLOS location. One advantage is the reuse of the anchor station for the microwave imaging setup to get low-resolution results for calibration. In addition, the reconfigurable anchor-station setup could be suitable in any NLOS environment with the predefined database. The database could also be improved even after the hardware system is set up. Furthermore, since the radar-based UWB microwave imaging technique uses a short pulse of low-power microwaves in the frequency range 3 GHz to 10 GHz, the measured scattered signal in the far-field can be used for imaging specific material according to its dielectric constant.
However, since the power level of the signal is limited, in part due to safety regulations, it is only detected over a short distance. The UWB pulse has a large bandwidth and, as such, the reflected signals contain a significant amount of information about the target for further imaging applications. Moreover, the anchor-station configuration model can be scaled by a factor suitable for the dimensions of any room or area under observation for a variety of indoor location applications.
A couple of important points to note is that although it is a radio technique, UWB is license-free because of its low power, and UWB technology’s carrier-less transmission property offers the advantage of simple and compact hardware.
Importantly, the performance of our proposed technique achieves more accurate localization of humans, for example, by using two body-worn transmitting antennas, one on each shoulder. The reconfigurable hardware structure under computer control provides the potential for a self-upgrading platform for indoor positioning with a more appropriate anchor-station setup being achieved using machine learning technology.
ACKNOWLEDGMENTS
The authors thank Iain Gold of the School of Engineering, University of Edinburgh, for his help in the fabrication and measurements of the antennas. The authors also acknowledge the Scottish Microelectronics Centre at the University of Edinburgh for measurement equipment support. This article is based on the paper “Localisation of Wearable Ultra-wideband Antenna for Indoor Positioning Application” presented at ION GNSS+ 2017, the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 25–29, 2017.
FENGZHOU WANG received a B.S. (Hons.) degree in electrical engineering from Birmingham City University in England, and an M.S. degree from the University of Southampton, England. He is working towards a Ph.D. degree in the School of Engineering, University of Edinburgh, Scotland. His research addresses the area of RF sensor systems design and integration.
GUOHUA WANG received his B.S. degree in machinery design and manufacture from Southwest Agricultural University, Chongqing, China; an M.S. degree in agricultural mechanization engineering from China Agricultural University, Beijing, China; and a Ph.D. degree in measurement technology and instrumentation from Beihang University, Beijing, China. He is a lecturer in the School of Instrumentation and Opto-Electronic Engineering in Beihang University. His research interests include automatic testing and partially reconfigurable systems.
“Hybrid Positioning with Smartphones” by J. Liu in Ubiquitous Positioning and Mobile Location-Based Services in Smart Phones, edited by R. Chen, published by IGI Global, Hershey, Pennsylvania, 2012, pp. 159–194.
Ubiquitous Positioning by R. Mannings, published by Artech House, Norwood, Massachusetts, 2008.
“Non-GPS Navigation for Security Personnel and First Responders” by L. Ojeda and J. Borenstein in Journal of Navigation, Vol. 60, No. 3, September 2007, pp. 391–407, doi: 10.1017/S0373463307004286.
• Ultra-Wideband Positioning
“Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis” by A.R.J. Ruiz and F.S. Granja in IEEE Transactions on Instrumentation and Measurement, Vol. 66, No. 8, pp. 2106–2117, August 2017, doi: 10.1109/TIM.2017.2681398.
Ultra-wideband Positioning Systems: Theoretical Limits, Ranging Algorithms, and Protocols by Z. Sahinoglu, S. Gezici and I. Guvenc, published by Cambridge University Press, Cambridge, U.K., 2008.
“Prior Models for Indoor Super-resolution Time of Arrival Estimation” by D. Humphrey and M. Hedley in Proceedings of VTC Spring 2009, the 69th Vehicular Technology Conference, Barcelona, Spain, April 26–29, 2009, 5 pp., doi: 10.1109/VETECS.2009.5073817.
“Ranging with Ultrawide Bandwidth Signals in Multipath Environments” by D. Dardari, A. Conti, U. Ferner, A. Giorgetti and M.Z. Win in Proceedings of the IEEE, Vol. 97, No. 2, February 2009, pp. 404–426, doi: 10.1109/JPROC.2008.2008846.
“A New Time of Arrival Estimation Method Using UWB Dual Pulse Signals” by R. Zhang and X. Dong in IEEE Transactions on Wireless Communications, Vol. 7, No. 6, June 2008, pp. 2057–2062, doi: 10.1109/TWC.2008.070112.
“Threshold-based TOA Estimation for Impulse Radio UWB Systems” by I. Guvenc and Z. Sahinoglu in Proceedings of ICU 2005, IEEE International Conference on Ultra-Wideband, Zurich, Switzerland, Sept. 5–8, 2005, pp. 420-425, doi: 10.1109/ICU.2005.1570024
Ultrawideband Antennas for Microwave Imaging Systems by T.A. Denidni and G. Augustin, published by Artech House, Norwood, Massachusetts, 2014.
“The Vivaldi Aerial” by P.J. Gibson in Proceedings of the 9th European Microwave Conference, Brighton, U.K., Sept. 17–20, 1979, pp. 101–105, doi: 10.1109/EUMA.1979.332681.
• Characteristics of Antennas and Their Interaction with Humans
In this month’s column, we travel along the road of PPP development, examine its current status and look at where it might go in the near future
By Sunil Bisnath, John Aggrey, Garrett Seepersad and Maninder Gill
Innovation Insights with Richard Langley
PPP. It’s one of the many acronyms (or initialisms, if you prefer) associated with the uses of global navigation satellite systems. It stands for precise point positioning. But what is that? Isn’t all GNSS positioning precise? Well, it’s a matter of degree.
Take GPS, for example. The most common kind of GPS signal use, that implemented in vehicle “satnav” units; mobile phones; and hiking, golfing and fitness receivers, is to employ the L1 C/A-code pseudorange (code) measurements along with the broadcast satellite orbit and clock information to produce a point position.
Officially, this is termed use of the GPS Standard Positioning Service (SPS). It is capable of meter-level positioning accuracy under the best conditions. There is a second official service based on L1 and L2 P-code measurements and broadcast data called the Precise Positioning Service (PPS).
In principle, because the P-code provides somewhat higher precision code measurements and the use of dual-frequency data removes virtually all of the ionospheric effect, PPS is capable of slightly more precise (and accurate) positioning. But because the P-code is encrypted, PPS is only available to so-called authorized users.
While meter-level positioning accuracy is sufficient for many, if not most applications, there are many uses of GNSS such as machine control, surveying and various scientific tasks, where accuracies better than 10 centimeters or even 1 centimeter are needed. Positioning accuracies at this level can’t be provided by pseudoranges alone and the use of carrier-phase measurements is required. Phase measurements are much more precise than code measurements although they are ambiguous and this ambiguity must be estimated and possibly resolved to the correct integer value.
Traditionally, phase measurements (typically dual-frequency) made by a potentially moving user receiver have been combined with those from a reference receiver at a well-known position to produce very precise (and accurate) positions. If done in real time (through use of a radio link of some kind), this technique is referred to as real-time kinematic or RTK.
A disadvantage of RTK positioning is that it requires reference station infrastructure including a radio link (such as mobile phone communications) for real-time results. Is there another way? Yes, and that’s PPP. PPP uses the more precise phase measurements (along with code measurements initially) on at least two carrier frequencies (typically) from the user’s receiver along with precise satellite orbit and clock data derived, by a supplier, from a global network. Precision, in this case, means a horizontal position accuracy of 10 centimeters or better.
In this month’s column, we travel along the road of PPP development, examine its current status, and look at where it might go in the near future.
In a 2009 GPS World “Innovation” article co-authored by Sunil Bisnath, the performance and technical limitations at the time of the precise point positioning (PPP) GPS measurement processing technique were described and a set of questions asked about the potential of PPP, especially with regard to the real-time kinematic (RTK) measurement processing technique.
Since the 2009 article, we’ve seen a significant amount of research and development (R&D) activity in this area. Many scientific papers discuss PPP and making use of PPP — a search on Google Scholar for “GNSS PPP” delivers nearly 7,000 results, and for “GPS PPP” more than 15,000 results! Will PPP eventually overtake RTK as the de facto standard for precise (that is, few centimeter-level) positioning? Or, in light of PPP R&D developments, should we be asking different questions, such as will multiple precise GNSS positioning techniques compete or complement each other or perhaps result in a hybrid approach?
In almost a decade, have we seen much in the way of positioning performance improvement, where “performance” can refer to positioning precision, accuracy, availability and integrity? Or, to some users, has the Achilles’ heel of PPP — the initial position solution convergence period — only been reduced from, for example, 20 minutes to 19 minutes? From such a perspective, all of this PPP research might not appear to have produced much tangible benefit. Advances have been made from this research and we will explore them here. Also, aside from many researchers working diligently on their own PPP software, there are now a number of well-established PPP-based commercial services — a number that has grown and been affected by the wave of GNSS industry consolidation over the decade. Consequently, there is much more to this story.
This month’s article summarizes the current status of PPP performance and R&D, and discusses the potential future of the technique. In the first part of the article, we will present brief explanations of conventional dual-frequency PPP, recent research and implementations, and application of the evolved technique to low-cost hardware. We will conclude the article with a rather dangerous attempt at near-term extrapolation of potential upcoming developments and conceivable implications.
Conventional PPP
The concept of PPP is based on standard, single-receiver, single-frequency point positioning using pseudorange (code) measurements, but with the meter-level satellite broadcast orbit and clock information replaced with centimeter-level precise orbit and clock information, along with additional error modeling and (typically) dual-frequency code and phase measurement filtering. Back in 1995, researchers at Natural Resources Canada were able to reduce GPS horizontal positioning error from tens of meters to the few-meter level with code measurements and precise orbits and clocks in the presence of Selective Availability (SA). Subsequently, the Jet Propulsion Laboratory introduced PPP as a method to greatly reduce GPS measurement processing time for large static networks. When SA was turned off in May 2000 and GPS satellite clock estimates could then be more readily interpolated, the PPP technique became scientifically and commercially popular for certain precise applications.
Unlike static relative positioning and RTK, conventional PPP does not make use of double-differencing, which is the mathematical differencing of simultaneous code and phase measurements from reference and remote receivers to greatly reduce or eliminate many error sources. Rather, PPP applies precise satellite orbit and clock corrections estimated from a sparse global network of satellite tracking stations in a state-space version of a Hatch filter (in which the noisy, but unambiguous, code measurements are filtered with the precise, but ambiguous, phase measurements). This filtering is illustrated in FIGURE 1, where measurements are continually added in time in the range domain, and errors are modeled and filtered in the position domain, resulting in reduced position error in time.
FIGURE 1. Illustration of conventional PPP measurement and error modeling in state-space Hatch filter, resulting in reduced position error in time.
The result is the characteristic PPP initial convergence period seen in FIGURE 2, where the position solution is initialized as a sub-meter, dual-frequency code point positioning solution, quickly converging to the decimeter-level in something like 5 to 20 minutes, and a few centimeters after ~20 minutes when geodetic-grade equipment is used (at station ALGO, Algonquin Park, Canada, on Jan. 2, 2017). For static geodetic data, daily solutions are typically at the few millimeter-level of accuracy in each Cartesian component.
FIGURE 2. Conventional geodetic GPS PPP positioning performance characteristics of initial convergence period and steady state for station ALGO, Algonquin Park, Canada, on Jan. 2, 2017.
The primary benefit of conventional PPP is that with the use of state-space corrections from a sparse global network, there is the appearance of precise positioning from only a single geodetic receiver.
Therefore, baseline or network RTK limitations are removed in geographically challenging areas, such as offshore, far from population centers, in the air, in low Earth orbit, and so on, and without the need for the requisite terrestrial hardware and software infrastructure. PPP is now the de facto standard for precise positioning in remote areas or regions of low economic density, which limit or prevent the use of relative GNSS, RTK or network RTK, but allow for continuous satellite tracking. These benefits translate into the main commercial applications of offshore positioning, precision agriculture, geodetic surveys and airborne mapping, which also are not operationally bothered by initial convergence periods of tens of minutes.
For urban and suburban applications, RTK and especially network RTK allow for near-instantaneous, few-centimeter-level positioning with the use of reference stations and regional satellite (orbit and clock) and atmospheric corrections. The use of double-differencing and these local or regional corrections allows sufficient measurement error mitigation to resolve double-differenced phase ambiguities. All of this additional information is not available to conventional PPP, limiting its precise positioning performance, but which is considered in PPP enhancements.
Progress on PPP Convergence Limitations
Over the past decade or so, PPP R&D activity can be categorized as follows:
Integration of measurements from multiple GNSS constellations, transitioning from GPS PPP to GNSS PPP;
Resolution of carrier-phase ambiguities in PPP user algorithms — in an effort to increase positional accuracy and solution stability, but foremost in an effort to reduce the initial convergence period; and
Use of a priori information to reduce the initial convergence and re-convergence periods and improve solution stability, making use of available GNSS error modeling approaches.
Unlike relative positioning, which makes use of measurements from the user receiver as well as the reference receiver, PPP only relies on measurements from the user site. This situation results in weaker initial geometric strength, and so the addition of more unique measurements is welcome. To make use of measurements from all four GNSS constellations (GPS, GLONASS, Galileo and BeiDou), user-processing engines must account for differences in spatial and temporal reference systems between constellations and numerous equipment delays between frequencies and modulations. The former can be done so that any number of measurements from any number of constellations can be processed to produce one unique PPP position solution. The latter requires a great deal of calibration, especially for heterogeneous tracking networks and user equipment (antenna, receiver and receiver firmware), most notably for the current frequency division multiple access GLONASS constellation.
FIGURE 3 shows typical multi-GNSS float (non-ambiguity-fixed) horizontal positioning performance at multi-GNSS station GMSD in Nakatane, Japan, on March 24, 2017. As with all modes of GNSS data processing, more significant improvement with additional constellations can be seen in sky-obstructed situations.
FIGURE 3. Typical conventional multi-GNSS PPP float horizontal positioning accuracy for station GMSD, Nakatane, Japan, March 24, 2017 (G: GPS, R: GLONASS, E: Galileo and C: BeiDou).
Related to multi-constellation processing is triple-frequency processing afforded by the latest generation of GPS satellites and the Galileo and BeiDou constellations. More frequencies mean more measurements, although with the same satellite-to-receiver measurement geometry as dual-frequency measurements. Again, additional signals require additional equipment delay modeling, in this case especially for the processing of GPS L1, L2 and L5 observables.
For processing of four-constellation data available from 20 global stations in early 2016, FIGURE 4 shows the average reduction of float (non-ambiguity-fixed) horizontal error from dual- to triple-frequency processing of approximately 40% after the first five minutes of measurement processing. In terms of positioning, this result, for this time period with a limited number of triple-frequency measurements, means a reduction in average horizontal positioning error from 43 to 26 centimeters within the first five minutes of data collection.
FIGURE 4. Average dual- and triple-frequency static, float PPP horizontal solution accuracy for 20 global stations. Data collected from tracked GPS, GLONASS, Galileo and BeiDou satellites in early 2016.
PPP with ambiguity resolution, or PPP-AR, was seen as a potential solution to the PPP initial solution convergence “problem” analogous to AR in RTK. Various researchers put forward methods, in the form of expanded measurement models, to isolate pseudorange and carrier-phase equipment delays to estimate carrier-phase ambiguities. These methods remove receiver equipment delays through implicit or explicit between-satellite single-differencing and estimate satellite equipment delays in the network product solution either as fractional cycle phase biases or altered clock products.
FIGURE 5 illustrates the difference between a typical GPS float and fixed solution (for station CEDU, Ceduna, Australia, on June 28, 2017). Initial solution convergence time is reduced, and stable few-centimeter-level solutions are reached sooner. For lower quality data, ambiguity fixing does not provide such quick initial solution convergence. Fixing is dependent on the quality of the float solution; and, for PPP, the latter requires time to reach acceptable levels of accuracy. Therefore, depending on the application, PPP-AR may or may not be helpful.
FIGURE 5. Typical float (red) and fixed (pink) GPS PPP horizontal solution error at geodetic station CEDU, Ceduna, Australia, on June 28, 2017.
To consistently reduce the initial solution convergence period, PPP processing requires additional information, as is the case for network RTK, in which interpolated satellite orbit, ionospheric and tropospheric corrections are needed since double-differenced RTK baselines over 10 to 15 kilometers in length contain residual atmospheric errors too large to effectively and safely resolve phase integer ambiguities. For PPP, uncombining the ionospheric-free code and phase measurements from the conventional model is required, to directly estimate slant ionosphere propagation terms in the filter state.
In this form, the model can allow for very quick re-initialization of short data gaps by using the pre-gap slant ionospheric (and zenith tropospheric) estimates as down-weighted a priori estimates post-gap — making these estimates bridging parameters in the estimation filter. Expanding this approach, external atmospheric models can be used to aid with initial solution convergence.
FIGURE 6 illustrates, for a large dataset, that applying a spatially and temporally coarse global ionospheric map (GIM) to triple-frequency, four-constellation float processing can reduce one-sigma convergence time to 10 centimeters horizontal positioning error from 16 to 6 minutes. If local ionospheric (and tropospheric) corrections are available and AR is applied, PPP (sometimes now referred to as PPP-RTK) can produce RTK-like results with a few minutes of initial convergence to few-centimeter-level horizontal solutions.
FIGURE 6. Averaged horizontal error from 70 global sites in mid-2016 using four-constellation, triple-frequency processing.
PPP Processing with Low-Cost Hardware
As the impetus for low-cost, precise positioning and navigation for autonomous and semi-autonomous platforms (such as land vehicles and drones) continues to grow, there is interest in processing such low-cost data with PPP algorithms. For example, it has been shown that with access to single-frequency code and phase measurements from a smartphone, short-baseline RTK positioning is possible. It has also been shown that similar smartphone data can be processed with the PPP approach. From the origins of PPP, it may be argued that single-frequency processing and many-decimeter-level positioning performance is not “precise.” But we will avoid such semantic arguments here (but see “Insights”), and focus on the use of high-performance measurement processing algorithms to new low-cost hardware. We are currently witnessing great changes in the GNSS chip market: single-frequency chips for tens-of-dollars or less; and boards with multi-frequency chips for hundreds-of-dollars. And these chips will continue to undergo downward price pressure with increases in capability, and be further enabled for raw measurement use in a wider range of applicable technology solutions. There are now a number of low-cost, dual-frequency, multi-constellation products on the market, with additional such products as well as smartphone chips coming soon.
To process data from such products with a PPP engine, modifications are required to optimally account for single-frequency measurements in the estimation filter, optimize the measurement quality control functions for the much noisier code and phase measurements compared to data from geodetic receivers, and optimize the stochastic modeling for the much noisier code and phase measurements. The single-frequency measurement model can be modified to either make use of the Group and Phase Ionospheric Calibration linear combination (commonly referred to as GRAPHIC) or ingest data from an ionospheric model. Due to the use of low-cost antennas, as well as the low-cost chip signal processing hardware, code and phase measurements suffer from significant multipath and noise at lower signal strengths; therefore, outlier detection functions must be modified. Also, the relative weighting of code and phase measurements must be customized for more realistic low-cost data processing.
FIGURE 7 compares the carrier-to-noise-density ratio (C/N0) values from ~1.5 hours of static GPS L1 signals collected from a geodetic receiver with a geodetic antenna, a low-cost receiver chip with a patch antenna, and a tablet chip and internal antenna, as a function of elevation angle. Received signal C/N0 values can be used as a proxy for signal precision. The three datasets were collected at the same time in mid-September 2017 in Toronto, Canada, with the receivers and antennas within a few meters of each other. The shading represents the raw estimates output from each receiver, while the solid lines are moving-average filtered results.
FIGURE 7. Carrier-to-noise-density ratios of ~1.5 hour of static GPS L1 signals from a geodetic receiver with a geodetic antenna, a low-cost receiver chip with a patch antenna, and a tablet chip and internal antenna, as a function of elevation angle.
Keeping in mind the log nature of C/N0, the high measurement quality of the geodetic antenna and receiver are clear. The low-cost chip and patch antenna signal strength structure is similar, but, on average, 3.5 dB-Hz lower. And the tablet received signal strength is lower still, on average a further 4.0 dB-Hz lower, with greater degradation at higher signal elevation angles and much greater signal strength variation.
The PPP horizontal position uncertainty for these datasets is shown in FIGURE 8. Note that reference coordinates have been estimated from the datasets themselves, so potential biases, in especially the low-cost and tablet results, can make these results optimistic. Given that only single-frequency GPS code and phase measurements are being processed, initial convergence periods are short and horizontal position error reaches steady state in the decimeter range. The geodetic and the low-cost results are comparable at the 2-decimeter level, whereas the tablet results are worse, at the approximately 4-decimeter level. Initial convergence of the geodetic solution is superior to the others, driven by the higher quality of its code measurements. The grade of antenna plays a large role in the quality of these measurements, for which there are physical limitations in design and fabrication. While geodetic antennas can be used, this is not always feasible, given the mass limitations of certain platforms or the cost limitations for certain applications.
FIGURE 8. Horizontal positioning error (compared to final epoch solutions) for geodetic, low-cost and tablet data processed with PPP software customized for single-frequency and less precise measurements.
Comments Regarding the Near Future
The PPP GNSS measurement processing approach was originally designed to greatly reduce computation burden in large geodetic networks of receivers by removing the need for network baseline processing. The technique found favor for applications in remote areas or regions with little terrestrial infrastructure, including the absence of GNSS reference stations. Given PPP’s characteristic use of a single receiver for precise positioning, various additional augmentations have been made to remove or reduce solution initialization and re-initialization interval to near RTK-like levels. But, to what end?
This question can be approached from multiple perspectives. From the theoretical standpoint, there is the impetus to maximize performance — millimeter-level static positioning over many hours, and few-centimeter-level kinematic positioning in a few minutes — by augmenting PPP in any way necessary. There is the academic exercise of maximizing performance without the need for local or regional reference stations – apparent single-receiver positioning, or truly wide-area augmentation. In terms of engineering problems, we can work to do more with less, that is, decimeter-level positioning with ultra-low-cost hardware, or the same with less, that is, few-centimeter-level positioning with low-cost hardware. And from the practical or commercial aspect, the great interest is for the implementation of evolved PPP methods for applications that can efficiently and effectively make use of the technology.
In terms of service providers, be it regional or global, commercial or public, there is momentum to provide enhanced correction products that are blurring the lines across the service spectrum from constellation-owner tracking to regional, terrestrial augmentation. A public GNSS constellation-owner, through its constellation tracking network, can provide PPP-like corrections and services. A global commercial provider with or without regional augmentation can provide similar services. The key is providing multi-GNSS state-space corrections for satellite orbits, satellite clocks, satellite equipment delays (fractional phase biases), zenith ionospheric delay and zenith tropospheric delay at the temporal and spatial resolution necessary for the desired positioning performance at reasonable cost, that is, subscription fees that particular markets can bear.
Given these correction products, PPP users have a greater ability to access a wide array of positioning performance levels for various new applications, be it few-decimeter-level positioning on mobile devices to few-centimeter-level positioning for autonomous or semi-autonomous land, sea and air vehicles. PPP can be used for integrity monitoring and perhaps safety-of-life applications where low-cost is a necessity and relatively precise positioning for availability and integrity purposes is required. For safety critical and high-precision applications, such as vehicle automation, PPP can be used alongside, or in combination with, RTK for robustness and independence with low-cost hardware. Such a parallel and collaborative approach would require a hybrid user processing engine and robust state-space corrections from a variety of local, regional and global sources, as we are seeing from some current geodetic hardware-based commercial services.
Near-future trends should also include more low-cost, multi-sensor integration with PPP augmentation. Optimized navigation algorithms and efficient user processing engines will be a priority as the capabilities of low-cost equipment continue to increase and low-cost integrated sensor solutions are required for mass-market applications. Analogous to meter-level point position GNSS, lower hardware costs should drive markets to volume sales, PPP-like correction services, and GNSS-based multi-sensor integration into more navigation technology solutions for various industry and consumer applications.
Clearly, the future of PPP continues to be bright.
SUNIL BISNATH is an associate professor in the Department of Earth and Space Science and Engineering at York University, Toronto, Canada. For over twenty years, he has been actively researching GNSS processing algorithms for a wide variety of positioning and navigation applications.
JOHN AGGREY is a Ph.D. candidate in the Department of Earth and Space Science and Engineering at York University. He completed his B.Sc. in geomatics at Kwame Nkrumah University of Science and Technology, Ghana, and his M.Sc. at York University. His research currently focuses on the design, development and testing of GNSS PPP software, including functional, stochastic and error mitigation models.
GARRETT SEEPERSAD is a navigation software design engineer for high-precision GNSS at u-blox AG and concurrently is completing his Ph.D. in the Department of Earth and Space Science and Engineering at York University. His Ph.D. research focuses on GNSS PPP and ambiguity resolution. He completed his B.Sc. in geomatics at the University of the West Indies in Trinidad and Tobago. He holds an M.Sc. degree in the same field from York University.
MANINDER GILL is a geomatics designer at NovAtel Inc. and concurrently is completing his M.Sc. in the Department of Earth and Space Science and Engineering at York University. His M.Sc. research focuses on GNSS PPP and improving positioning accuracy for low-cost GNSS receivers. He holds a B.Eng. degree in geomatics engineering from York University.
FURTHER READING
• Comprehensive Discussion of Technical Aspects of Precise Point Positioning
“Precise Point Positioning” by J. Kouba, F. Lahaye and P. Tétreault, Chapter 25 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
• Earlier Precise Point Positioning Review Article
“Precise Point Positioning Using IGS Orbit and Clock Products” by J. Kouba and P. Héroux in GPS Solutions, Vol. 5, No. 2, October 2001, pp. 12–28, doi: 10.1007/PL00012883.
“Precise Point Positioning for the Efficient and Robust Analysis of GPS Data from Large Networks” by J.F. Zumberge, M.B. Heflin, D.C. Jefferson, M.M. Watkins and E.H. Webb in Journal of Geophysical Research, Vol. 102, No. B3, pp. 5005–5017, 1997, doi: 10.1029/96JB03860.
“Reduction of PPP Convergence Period Through Pseudorange Multipath and Noise Mitigation” by G. Seepersad and S. Bisnath in GPS Solutions, Vol. 19, No. 3, March 2015, pp. 369–379, doi: 10.1007/s10291-014-0395-3.
“Global and Regional Ionospheric Corrections for Faster PPP Convergence” by S. Banville, P. Collins, W. Zhang and R.B. Langley in Navigation, Vol. 61, No. 2, Summer 2014, pp. 115–124, doi: 10.1002/navi.57.
“A New Method to Accelerate PPP Convergence Time by Using a Global Zenith Troposphere Delay Estimate Model” by Y. Yao, C. Yu and Y. Hu in The Journal of Navigation, Vol. 67, No. 5, September 2014, pp. 899–910, doi: 10.1017/S0373463314000265.
“External Ionospheric Constraints for Improved PPP-AR Initialisation and a Generalised Local Augmentation Concept” by P. Collins, F. Lahaye and S. Bisnath in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 17–21, 2012, pp. 3055–3065.
“Integer Ambiguity Resolution on Undifferenced GPS Phase Measurements and Its Application to PPP and Satellite Precise Orbit Determination” by D. Laurichesse and F. Mercier, J.-P. Berthias, P. Broca and L. Cerri in Navigation, Vol. 56, No. 2, Summer 2009, pp. 135–149.
“Resolution of GPS Carrier-phase Ambiguities in Precise Point Positioning (PPP) with Daily Observations” by M. Ge, G. Gendt, M. Rothacher, C. Shi and J. Liu in Journal of Geodesy, Vol. 82, No. 7, July 2008, pp. 389–399, doi: 10.1007/s00190-007. Erratum: doi: 10.1007/s00190-007-0208-3.
“Isolating and Estimating Undifferenced GPS Integer Ambiguities” by P. Collins in Proceedings of ION NTM 2008, the 2008 National Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 28–30, 2008, pp. 720–732.
• Precise Positioning Using Smartphones
“Positioning with Android: GNSS Observables” by S. Riley, H. Landau, V. Gomez, N. Mishukova, W. Lentz and A. Clare in GPS World, Vol. 29, No. 1, January 2018, pp. 18 and 27–34.
By Peter Steigenberger, Steffen Thoelert, André Hauschild, Oliver Montenbruck and Richard B. Langley
INNOVATION INSIGHTS with Richard Langley
POP QUIZ: What is the most populous metropolitan area in the world? According to Wikipedia, it is Tokyo. In fact, Japan has three cities in the list of the 50 largest cities in the world. Not only are there a lot of people in these cities, they also have many tall and densely packed buildings. And that’s a problem for GPS and the other global navigation satellite systems.
Radio signals travel in straight lines. Well, mostly so. At very low frequencies, radio waves propagate as ground waves and can achieve long-distance propagation in the waveguide formed by the surface of the Earth and the ionosphere. At slightly higher frequencies, such as those used by AM radio, signals still travel as ground waves. However, additionally, the signals propagate upwards as skywaves. During daylight hours, the D layer of the ionosphere absorbs the skywaves, but when the D layer dissipates at night, the higher ionospheric levels can reflect skywaves back to Earth allowing long-distance reception. And communication by shortwave is virtually all by ionosphere-bounce skywaves. Above 30 MHz or so, signals normally travel along line-of-sight raypaths. The atmosphere can slightly bend the raypath, but the signals essentially travel in straight lines. Of course, that’s what makes GPS possible.
GPS works exceedingly well as long as a receiver’s antenna has a line-of-sight “view” of the satellites. Obstacles such as mountains and buildings block the relatively weak GPS signals. In concrete canyons, for example, that may leave a receiver with fewer than four satellites in view, meaning that 3D positioning is impossible. Even if four or more satellites are visible, they may be bunched together in the sky, resulting in high dilution of precision values and potentially large position errors.
In an effort to alleviate the GPS positioning problem in both urban and mountainous areas of Japan, the Japanese government has developed the Quasi-Zenith Satellite System (QZSS). A constellation of three inclined geosynchronous orbit (IGSO) satellites and one geostationary satellite transmits GPS-compatible signals to enhance positioning availability and accuracy. The IGSO satellites have repeating figure-eight ground tracks with the satellites spending most of their one-sidereal-day orbit, centered around apogee, over the Japanese archipelago. The satellites sequentially hover in the sky near the zenith for long periods of time. The satellites also provide both standard and advanced augmentation signals.
The first, or prototype, Block I QZSS satellite was launched in 2010 and, based on the positive test results from this satellite, an additional three satellites were launched in 2017, completing a four-satellite constellation. In this month’s column, we examine the recent developments of this unique and innovative navigation system.
With the launch of two additional spacecraft in August and October 2017, the Japanese Quasi-Zenith Satellite System (QZSS) reached the goal of a four-satellite constellation with the first fully-operational services expected to start in 2018. Aug. 19, 2017, marked the launch of QZS-3, the first geostationary Earth orbit (GEO) QZSS satellite, while the third spacecraft in inclined geosynchronous orbit (IGSO), QZS-4, was subsequently launched on Oct. 10, 2017. An artist’s view of the constellation is shown in FIGURE 1.
FIGURE 1. An artist’s view of the QZSS satellites. The upper-most satellite is the geostationary QZS-3 spacecraft with the additional S-band dish antenna whereas the other satellites pictured are the inclined geosynchronous satellites. (Image: Mitsubishi Electric)
Table 1 lists the four satellites of the current QZSS constellation. Whereas the first generation Block I satellite QZS-1 was launched in 2010, the three Block II satellites joined the constellation in 2017.
Table 1. QZSS constellation as of December 2017. SVN: space vehicle number, PRN: pseudorandom noise (code number), IGSO: inclined geosynchronous orbit, GEO: geostationary Earth orbit.
The most obvious visual difference between the QZSS Block I and II satellites is the different number of subpanels for the solar arrays: three for the Block I satellite and two for the Block II satellites with spanned widths of 25.3 meters and 19.0 meters, respectively. The reduced size of the Block II array has been achieved through the use of new, high-efficiency solar cells. The GEO satellite in addition carries S- and Ku-band antennas with diameters of 3.2 meters and 1.0 meter, respectively. While the IGSO satellites are equipped with a helix antenna array for transmission of the main L-band navigation signals, the GEO satellite uses a patch antenna array similar to that of the Galileo satellites.
The ground tracks of the four QZSS satellites are plotted in FIGURE 2. The ground tracks of all of the IGSO satellites have the characteristic figure-eight shape due to the large orbit eccentricity of 0.075 and results in a longer visibility period for users in the northern hemisphere. The ground tracks do not precisely match, however. QZS-1 and QZS-4 have similar orbit inclinations (with respect to the equator) of 40.9° and 40.5°. QZS-2, on the other hand, has a larger inclination of 44.5°, which leads to a wider extension of the ground track in the north-south direction.
FIGURE 2. Ground tracks of the four-satellite QZSS constellation as of Dec. 4, 2017. The blue square indicates the sub-satellite point of the geostationary QZS-3 satellite. (Image: Authors)
Also, the central longitude of the ground tracks, which marks the center of the figure-eight shape, varies between 130° and 140° E. These differences are still within the tolerances defined in the QZSS Interface Specification, version 1.8 of Oct. 3, 2016, which specifies the inclination to be 43° ± 4° and the central longitude of the ground track to be 135° ± 5° E. The GEO satellite QZS-3 is located at 127° E and has been controlled to stay within a 0.1° inclination window since achieving its initial orbit.
All QZSS satellites transmit navigation signals in the L1, L2 and L5 bands compatible with GPS, namely L1 C/A, L1C, L2C and L5 (the Positioning, Navigation and Timing or PNT service). QZSS-specific signals are transmitted in the L1, L5 and L6 bands: the Sub-meter Level Augmentation Service or SLAS (formerly, Submeter-class Augmentation with Integrity Function or SAIF) signals for all satellites on L1 and, in addition, on L5 for Block II satellites (see TABLE 2).
Table 2. QZSS signals. The L2C and CLAS signals use interleaved bit streams for concurrent transmission of two independent ranging sequences. The L1S signal consists of SLAS, a message service, and L1Sb, an SBAS signal. (Based on Table 11.2 in the Springer Handbook of Global Navigation Satellite Systems).
Starting in 2020, the GEO satellite will also provide a satellite-based augmentation system (SBAS) signal called L1Sb with range corrections and integrity information for aviation applications in particular. The SLAS and SBAS signals are transmitted via dedicated antennas but they are phase coherent with the GPS-compatible navigation signals transmitted via the main L-band antenna. The L6 signal provides the Centimeter Level Augmentation Service or CLAS (formerly, the L-band Experiment or LEX) on all QZSS satellites, but employs a different signal structure for Block I (L61) and Block II (L62). An overview of the various L-band signals and corresponding PRN assignments is given in TABLE 3. QZS-3 also provides the QZSS Safety Confirmation Service (Q-ANPI) to support rescue operations with S-band communication in case of a disaster. The total transmit power is 500 watts for the Block II IGSO satellites and 550 watts for the GEO satellite.
Table 3. PRN code assignment of QZSS satellites according to the interface specifications (see Further Reading). RINEX: PRN code in RINEX observation files; NAV: PRN code for L1 C/A, L1C, L2C and L5 navigation signals; NSTD: non-standard codes of IGSO/GEO satellites.
QZS-3/4 SIGNAL TRANSMISSION
Tracking of the QZS-3 L1 C/A and L5 signals by receivers in the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt or DLR) and International GNSS Service networks started on Sept. 10, 2017, at 09:04 UTC followed by the L1C and L2C signals at 09:27 UTC. L5 tracking started with a very low carrier-to-noise-density ratio (C/N0) of 10 – 20 dB-Hz that increased to 50 – 55 dB-Hz shortly after the activation of the L1C and L2C signals. QZS-3 broadcast ephemerides were first transmitted on Oct. 4, 2017, at 16:00 UTC. However, tracking of the L1, L2 and L5 navigation signals with common geodetic receivers is currently limited to receivers with experimental firmware versions developed by three different manufacturers.
Signal transmissions from QZS-4 started on Nov. 1, 2017. The first L1 C/A signals of PRN J03 were received at 02:50 UTC. At the same time, L5 signal transmission started but this signal was only tracked by a very limited number of receivers due to its low signal strength resulting in a C/N0 of only about 15 dB-Hz. At 03:14 UTC, an increase of the C/N0 by about 40 dB occurred and many additional receivers started tracking the L5 signal. At the same time, the L1C and L2C signals were also activated followed by the L1 SLAS signal at 03:20 UTC.
It is interesting to note that QZS-4 also transmitted the non-standard code J06 on different frequencies during its first weeks of operation. This code cannot be used for positioning and is used for test purposes or in case of system errors. Until Nov. 27, 2017, QZS-4 regularly switched between transmission of standard and non-standard codes. An example of such a switch for the station UNX200AUS located in Sydney, Australia, is shown in FIGURE 3. During this test period, several outages of individual or all navigation signals also occurred. Since Nov. 24, 2017, 5:00 UTC, broadcast ephemerides of QZS-4 have been available and transmission of the L5 SLAS signal started at 09:31 UTC.
FIGURE 3. QZS-4 signals tracked by DLR’s JAVAD Delta-3TH receiver in Sydney, Australia. The top plot shows the standard code PRN J03 and the bottom plot the non-standard code J06. The measured C/N0 is shown for L1 C/A (black), L1C (blue), L2C (red) and L5 (green). (Image: Authors)
FIGURE 4 shows the L-band normalized power spectra of QZS-2 and QZS-4. The spectra were obtained from in-phase (I) and quadrature (Q) data recorded with DLR’s 30-meter high-gain antenna in Weilheim, Germany. Almost identical characteristics can be seen for the signals of both satellites in the L1, L2 and L6 bands. However, in the L5 band, QZS-4 shows a slightly lower power than that of QZS-2 due to the lack of the L5 SLAS transmission during the data recording. Unfortunately, QZS-3 is not visible from Weilheim due to a longitude difference of more than 115°.
FIGURE 4. Normalized power spectra of QZS-2 and QZS-4 measured with DLR’s 30-meter high-gain antenna on July 18, 2017, and Nov. 7, 2017, respectively. (Image: Authors)
ATTITUDE
Usually, QZS-2 and QZS-4 follow a nominal yaw steering attitude with the spacecraft z-axis pointing towards the Earth and the y-axis (solar panel axis) oriented perpendicular to the plane defined by the locations of the satellite, the Sun, and the Earth. The maximum yaw rate of these satellites is limited to 0.055° per second and can be exceeded by the nominal yaw rate when the angle of the Sun with respect to the orbital plane (the beta angle, β) is between -5° and +5°. During orbit control maneuvers, the QZSS Block II IGSO satellites are operated in orbit normal mode with the z-axis pointing to the Earth and the y-axis perpendicular to the orbital plane. The geostationary QZS-3 satellite is continuously operated in orbit normal model while QZS-1 enters orbit normal mode for |β| < 20°.
Detailed information about the different attitude rules as well as spacecraft reference frame, mass, center of mass, phase center offsets and variations of the navigation antenna, laser retroreflector offsets, satellite group delays as well as the total transmit power of all four satellites is provided by the Cabinet Office, Government of Japan, in the QZSS satellite information documents.
Since all QZSS satellites are equipped with a separate L1 SLAS transmit antenna, which is mounted with an offset to the main L-band antenna, each satellite’s attitude can be directly estimated from single-difference carrier-phase observations between the two spacecraft antennas.
FIGURE 5 illustrates the attitude of QZS-4 estimated from L1 C/A and L1 SLAS observations from 10 tracking stations as well as the nominal yaw steering attitude. QZS-4 had a beta angle of about 11° on Dec. 9, 2017, confirming that this satellite does not enter orbit normal mode for |β| < 20° as does QZS-1. Differences between nominal yaw steering attitude and estimated attitude are usually within ±1.5° reflecting estimation errors as well as differences between nominal and true attitude.
FIGURE 5. Nominal yaw steering attitude (blue) and estimated attitude (red) of QZS-4 for Dec. 9, 2017 (β ≈ 11°). (Image: Authors)
CLOCK PERFORMANCE
The clock stability represented by the modified Allan deviation is given in the upper panel of FIGURE 6 for the QZSS IGSO satellites. The QZSS Block II IGSO satellites show an almost identical stability for integration periods up to 100 seconds. For longer periods, the QZS-2 clock seems to perform slightly better.
However, this effect is probably related to the number of stations contributing to the clock solutions of the individual satellites which differs by a factor of more than two. For comparison purposes, the Allan deviation of two Galileo rubidium clocks (GAL-101 and GAL-204) and a Galileo passive hydrogen maser (PHM, GAL-207) are plotted in the bottom panel of Figure 6.
Whereas the performance of the QZSS and Galileo rubidium clocks is very similar, the Galileo PHM is more stable by a factor of two to five over all integration periods.
FIGURE 6. Modified Allan deviations of the QZSS IGSO rubidium clocks, Galileo rubidium clocks (GAL-101 and GAL-204) and a Galileo passive hydrogen maser (GAL-207). (Image: Authors)
CONCLUSIONS
With the launch of the third IGSO spacecraft and the first GEO spacecraft, the QZSS constellation has reached a four-satellite configuration, which is required for the provision of operational augmentation services. QZS-3 and QZS-4 were declared useable for PNT, SLAS, and CLAS trial services on Dec. 18, 2017, and Jan. 12, 2018, respectively. Inclusion in the operational QZSS constellation is expected for 2018 and this will provide continuous visibility of three satellites in the service area. An expansion to a constellation of seven satellites is planned for 2023 including a Public Regulated Service for authorized users.
MANUFACTURERS
Data used in this article was collected using Javad GNSS Delta-G3TH, Trimble NetR9 and Septentrio PolaRx4 and PolaRx5 receivers.
Authors Peter Steigenberger, Steffen Thoelert, André Hauschild and Oliver Montenbruck are from the German Aerospace Center (DLR).
Richard B. Langley is from the University of New Brunswick and authors the monthly “Innovation” column for GPS World magazine.
FURTHER READING
• Quasi-Zenith Satellite System
“Quasi-Zenith Satellite System” part of “Regional Systems” by S. Kogure, A.S. Ganeshan and O. Montenbruck, Chapter 11 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
“QZS-2 Signal Analysis, QZS-3 Launched” by S. Thoelert, A. Hauschild, P. Steigenberger, O. Montenbruck and R.B. Langley in GPS World, Vol. 28, No. 9, September 2017, pp. 10–14.
In this month’s column, we review the history and future of software-defined radios (SDRs), looking in particular at GNSS SDRs.
This online version of the print article includes two bonus sections for which there wasn’t room in the magazine: New Frontiers: GNSS SDRs in Space and The Economics of SDRs.
By James T. Curran, Carles Fernández-Prades, Aiden Morrison and Michele Bavaro
Innovation Insights with Richard Langley
I had a fairly normal childhood—as a nerd. I was interested in radio and so was my sister. For her, it was the local AM radio stations where she could hear the latest Beatles’ hits on her six-transistor handheld portable. But for me, it was shortwave radio. I received a Knight-Kit two-tube regenerative shortwave receiver for Christmas 1963 when I was 14. It used one tube for the RF section and one tube for the audio amplifier. Using a random-length antenna above my mother’s clothesline, I was able to log radio stations from more than 100 countries during my high-school days.
With the pressures of university studies and starting to work for a living, I put my radio hobby on hold. But on an Air Canada flight to a conference early in 1985, I spotted an advertisement in the inflight magazine for the diminutive Sony ICF-7600D portable shortwave receiver — the height of miniaturization of microprocessor-controlled receivers at the time — and I acquired one in Hong Kong in May of that year before starting a lecture tour in the People’s Republic of China. I used the Sony receiver extensively at home and on trips overseas and heard many interesting broadcasts over the years including President Gorbachev’s resignation speech live from Radio Moscow.
Fast forward to 2013, when I purchased my first software-defined radio (SDR) receiver, a FUNcube Dongle Pro+, with frequency coverage from longwave up to the L-band. Interfaced via USB to a computer and bespoke software, an SDR receiver allows one to monitor a wide swath of the radio spectrum or record it for future analysis as in-phase and quadrature components. I have since acquired several other SDR receivers, and the capability of these units keeps getting better and better, delighting me and my fellow radio hobbyists. But these improvements in SDR technology extend to other uses of the radio spectrum including GNSS. In this month’s column, we review the history and future of SDRs looking in particular at GNSS SDRs. And what the Beatles said about improving one’s nature as a human being also aptly describes the performance of SDRs: it’s getting better all the time.
The software-defined radio (SDR) has an infinite number of interpretations depending on the context for which it is designed and used. By way of a starting definition, we choose to use that of a reconfigurable radio system whose characteristics are partially or fully defined via software or firmware. In various forms, the SDR has permeated a wide range of user groups, from military and business to academia and the hobby radio community.
SDR technology has evolved steadily over the decades following its birth in the mid-1980s, with various surges of activity being generally aligned with new developments in related technologies (processor power, serial busses, signal processing techniques and SDR chipsets). At present, it appears that we are experiencing one such surge, and the GNSS SDR is expanding in many directions. The proliferation of collaboration and code-sharing sites such as GitHub has enabled communities to share and co-develop receiver technology; the rise in the maker-culture and crowdsourcing has led to the availability of high-performance radio-frequency (RF) front ends; and the adoption of SDRs by some major telecommunications companies has led to the availability of suitable integrated circuits.
These contributing factors have played a part in an increased uptake of GNSS SDRs in military, scientific and commercial applications. In this article, we explore the recent trends and the technology behind them.
SDR TOPOLOGIES
The software-defined radio for GNSS has evolved over the past decade, both in terms of the adoption of new frequencies, new signals and new systems, as they have become available; as well as the adoption of new processing platforms and their associated processing techniques. Shown in FIGURE 1 is a (simplified) depiction of how the topology of the software-defined GNSS receiver has evolved over the years (a–d) with a hint at where it might go next (e, f).
FIGURE 1. A simplified depiction of different SDR topologies (GPP = general-purpose processor, GPU = graphics processing unit, FPGA = field-programmable gate array, SoC = system on chip, RFSoM = radio-frequency system on module, RFSoC = radio-frequency system on chip).
In a traditional GNSS SDR, as depicted in Figure 1 (a), the RF front end typically interfaces with the general-purpose processor (GPP) through a standard bus, and intermediate-frequency (IF) samples are streamed to a buffer. Once on the GPP, basic operations such as correlation, acquisition/tracking, measurement generation and positioning were performed.
Of all of the operations performed by a GNSS receiver, correlation is (by some orders of magnitude) the most computationally intensive. However, the correlation operations are relatively simple, often requiring only integer arithmetic, and can be easily parallelized. When running on modern processors, optimized software receivers can avail themselves of multi-threading (task parallelism) or the operations can be vectorized to exploit data parallelism (single-instruction, multiple data).
Beyond a certain number of GNSS signals and a certain bandwidth, a GPP simply cannot cope, and many SDR receivers looked to hardware acceleration for the correlation process. This either took the form of a graphics processing unit (GPU), or a field-programmable gate array (FPGA), as depicted in Figure 1(b), both of which are well suited to highly parallel tasks. These processing platforms can be powerful and efficient, and so can almost alleviate all challenges associated with correlation. This is not the only way to alleviate the processing burden, as it is also possible to delegate the correlation task to a network of computers. This “cloud” receiver architecture, depicted in Figure 1(e), has received particular attention of late, showing promise for certain niche applications. This computation-in-the-cloud trend has partially reverted with the proliferation of many-core desktop and mobile processors, but at a certain level of signal or processing complexity, the extensions remain applicable.
Nowadays, data throughput becomes an important consideration. When considering multi-constellation, multi-frequency receivers, the objective is often to preserve signal quality, which implies high bandwidth and high digitizer resolution. A triple-frequency front end might easily produce in excess of 100 or even 500 megabytes per second. When this data is delivered to the GPP or somewhere in the host computer, and then offloaded to the GPU (or any other hardware accelerator), it might be handled twice, exacerbating the bottleneck. To overcome this problem (and for other practical architectural reasons) it can be preferable to interface the front end directly with the accelerator, where correlation was performed, and leave the brains of the receiver (including loop closure; data processing; and position, velocity and time computation) on the GPP. This is a particularly convenient approach when using an FPGA accelerator, as shown in Figure 1(d).
A similar architecture can be achieved using modern system-on-chip (SoC) integrated circuits (ICs), which can offer a large FPGA and a powerful GPP on the same piece of silicon, as depicted in Figure 1 (d). Indeed, a number of receivers using this architecture have seen commercial and scientific success, having many of the benefits of dedicated silicon while retaining the benefits of the software-defined radio (for example, the Swift Navigation Piksi Multi GNSS Module). Recent developments in the field have seen the world’s first RF system-on-module (RFSoM) or system-on-chip (RFSoC) devices, targeting 5G mobile communications applications. With an architecture similar to that of Figure 1(f), the IC touts up to eight inputs and eight outputs (8×8) multiple input, multiple output (MIMO) with 12-bit analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) running at rates of 2/4 gigasamples per second. Depending on how this trend evolves (assuming lighter versions become available), this might offer an exciting new platform for GNSS SDRs, simultaneously capable of multi-frequency and multi-antenna operation.
RF HARDWARE: THE ENABLER
GNSS SDRs see the world through a hardware peripheral, and the capability of this hardware defines the perimeter between what the receiver can and cannot do. In essence, the front-end peripheral converts one or more analog RF signals at the antenna to a stream or sequence of packets of digital-baseband/IF data to the GPP.
A software-defined radio for GNSS benefits greatly from being flanked in the RF spectrum on both sides by signals that are of interest to the civilian population. Applications such as Digital Video Broadcasting — Terrestrial (DVB-T) and Digital Video Broadcasting — Satellite Second Generation (DVB-S2) receivers have resulted in the availability of a wide range of low-cost RF ICs that are tunable to GNSS frequencies (typically spanning from 900 MHz to 2.1 GHz), which, along with dedicated GPS ICs, were at the heart of early GNSS SDR front ends. Later developments in ICs designed around the 2/3/4G mobile communications standards brought another generation of ICs, bringing higher instantaneous bandwidth, higher ADC resolution and MIMO, and re-transmit capability. With the increase in popularity of the software-defined radio for cognitive radio, Wi-Fi, 3G and Long-Term Evolution or LTE, and enjoying the benefits of a crowdfunding movement, a wide range of front-end peripherals quickly appeared. Many of these front ends are compatible with GNSS, offering significantly increased performance relative to their predecessors. A selection of some GNSS-compatible SDR peripherals (both new and old) is shown in TABLE 1.
TABLE 1. A selection of GNSS-compatible SDR front ends (Half duplex = transmit and receive but not simultaneously; Full duplex = transmit and receive simultaneously).
Reference Oscillators. Although many of the requirements of modern telecommunications ICs are beyond what is needed for GNSS (such as ADC resolution, frequency range, bandwidth and linearity), clock stability is often inadequate. Communications signals are generally received at high signal-to-noise ratio so the carrier can be easily recovered, even given very poor clock stability.
In contrast, clock stability can be critical for GNSS applications, due to the required comparatively long coherent integration period (greater than 1 millisecond) for a couple of reasons. Firstly, because the search-space granularity is related to the integration period and the size of the search space to the frequency uncertainty, clock accuracy is important, as an uncertainty of some tens of kHz might increase acquisition time. Secondly, the short-term stability is important as a large degree of phase wander can be challenging when attempting to track the carrier phase with a loop-update rate below 1 kHz. In fact, this issue was so pronounced on early RTL-SDR DVB-T front ends, that later revisions upgraded the quartz reference oscillator to a more respectable 0.5 parts per million temperature-compensated crystal oscillator (TCXO). Typically, a TCXO with an accuracy of better than 1 part per million is preferable, but this metric alone is far from sufficient.
Depending on the class of signals for which the SDR front end will be used, the characteristics of the oscillator, the configuration of its support electronics, and even whether the mixers and analog-to-digital conversion process use the same reference can vary. For example, not all TCXOs are suitable for GNSS applications due to the way in which they internally apply their temperature compensations. If a given TCXO uses a stepwise compensation configuration based on any form of digital feedback, the size of the resulting steps can severely impact the GNSS tracking loops. Even if a given TCXO has a suitable compensation curve and implementation, as well as low and acceptable intrinsic phase noise, every other link in the clock chain must preserve this performance. In some front-end implementations, swapping out a low-quality clock for a higher quality one is sufficient, but in others there can be design limitations in the oscillator power supply, the oscillator signal conditioning, subsequent clock generation steps, or distribution routing that can prevent the design from ever being suitable for GNSS use. This can be critical in cases where the carrier phase is of interest, for example, where phase coherence between channels is important for multi-frequency linear combinations, or for multi-antenna systems.
Fortunately, many modern SDR front ends support the use of an external clock. This feature can also be important when attempting to combine two front-end peripherals to effect a dual-frequency or dual-antenna software receiver.
The Bus. An intrinsic bottleneck for any SDR system is the fact that some form of connection or bus is needed to carry data from the collection point to the processing element. In a fully integrated system, this connection still exists, but it is typically a trace on a circuit board or even a pathway within an integrated device. In contrast, in an SDR this often takes the form of a cable or connector between the physically discrete system modules. In cases where the devices are discrete, it is often necessary to implement some data buffering on both ends of the bus.
The suitability of a particular bus is often determined by the sustained data throughput rate required by the application and, in some cases, the latency of the bus. An example of a number of interfaces popular in modern SDR front ends is shown in FIGURE 2, illustrating the nominal throughput and the minimum latency of each. In the case of a GNSS SDR, the minimum conceivable throughput required would be hundreds of megabytes per second, but a system could easily use in excess of 200 megabytes per second for multi-frequency, high-bit-depth data.
Of course, in post-processing applications, bus latency is not a factor. However, certain applications may require that this latency is small, or bounded, or somehow deterministic. Applications such as closed-loop vehicle control or certain safety systems might impose tight requirements on latency. High or unpredictable latency in GNSS measurements might lead to loop instability, in the case of a control system, or might erode safety margins. Although the trend in modern interfaces is for higher throughput, only certain interfaces offer low latency.
FIGURE 2. Bandwidth vs. latency scatter plot for popular buses.
The Silicon. In comparison with less-flexible fixed-function GNSS receiver chips, GNSS SDR hardware platforms provide the opportunity to exchange one to three orders of magnitude of power consumption and system size to gain substantial control over the characteristics of the design. Moreover, one of the other main differences between GNSS front ends and general purpose SDR front ends is the number of bits of ADC resolution and the conversion linearity. Both contribute to power consumption. However, it may be worth considering that GNSS-specific front ends have not received as much attention as telecommunications front ends and, consequently, there is at least a generational gap in silicon mask technology (most GNSS products are at the 350-nanometer level).
In terms of GNSS-specific devices, products such as the SiGe SE4110L, the Maxim MAX2769 and Saphyrion’s SM1027U provide a solution for slightly flexible L1 GPS, Galileo or, in some chip revisions, GLONASS operation. These kinds of chips support a few sampling rates and filtering configurations.
In the middle ground are the much more flexible chips from Maxim including the MAX2120 and MAX2112, which provide total L-band coverage, a myriad of filtering options, and adjustable gain control, all within a 0.3-watt power budget per channel (RF portion only). These chips allow for single-band coverage of adjacent GNSS signals such as GPS and GLONASS L1 or L2 in a single non-aliased RF band.
In terms of multi-channel options, devices such as the Maxim MAX19994A or the NTLab NT1065 offer dual- or quad-channel functionality, respectively. Similar functionality can be achieved by pairing downconversion and IF receiver ICs such as, for example, the Linear Technologies LTC5569 dual-active downconverting mixer and the Analog Devices AD6655 IF receiver, which might offer sufficient performance for high-accuracy dual-frequency positioning.
Higher up the cost, power and complexity structure are radios designed explicitly to support SDR applications that happen to cover GNSS bands such as the Lime LMS6002d/LMS7002M and the Analog Devices AD9364. Notably, these provide receive and transmit channels and frequency coverage up to 6 GHz.
Another interesting and relevant trend is in the use of direct RF sampling ICs, which offer the possibility of full L-band coverage and multi-antenna support. Examples include the Texas Instruments ADS54J40, which offers a dual-channel, 14-bit, 1.0-gigasamples-per-second ADC, or the LM97600 offering a 7.6 bit, quad-channel, 1.25-gigasamples-per-second ADC.
Future Trends, Limitations and Opportunities. Most of the innovation in SDR peripherals has taken place in the telecommunications domain. The GNSS SDR community, being comparatively small, has benefited from these innovations, insofar as they were applicable, but has had little influence over their design.
Looking at the bigger picture, it is clear that GNSS SDRs will simply have to follow the road paved by telecommunications SDRs. We will have to use what is made available, and so future trends in GNSS SDRs will likely be driven by the needs of the telecommunications SDR community.
So what are these trends and will they be aligned with GNSS trends? The answer seems to be yes and no. One of the bigger trends in modern GNSS receivers is the move to dual- or multi-frequency and a second trend is towards multi-antenna receivers for attitude determination or multi-element antennas for interference management. Meanwhile, telecommunications applications are almost universally using MIMO transceivers; however, they don’t seem to be using multiple (simultaneous) carriers.
What is particularly interesting is that the requirements for a MIMO transceiver are well aligned with that of a null-steering GNSS antenna: namely high linearity and high ADC resolution, and phase-coherence between channels (provided by, for example, the Lime Microsystems LMS7002M or the Analog Devices AD9361). As a result, it is possible (or even likely) that in the near future we will see more innovation in GNSS SDRs in the area of multi-antenna processing than in multi-frequency processing.
Signal Processing Techniques for SDRs. As mentioned above, signal correlation for acquisition and tracking is the most computationally intensive operation conducted by a GNSS receiver. In software receivers, many signal acquisition strategies are built around the fast Fourier transform (FFT) algorithm with a signal tracking rake of three or more correlators per signal. When targeting real-time processing, these operations need to be applied to a stream of signal samples arriving at a rate of many megasamples per second. This is a challenge for GPPs when implementing a multi-constellation, multi-frequency GNSS receiver.
The processing task can either be alleviated or accelerated. Assistance data can allow the receiver to reduce the size of the search acquisition space, thereby dramatically reducing the overall computational load. In many cases, the software receiver is running on a host computer with many connectivity options. Alternatively, a variety of options are available for accelerating the tasks.
Parallelization. The main approach for accelerating GNSS signal processing is parallelization. Shared-memory parallel computers can execute different instruction streams (or threads) on different processors, or by interleaving multiple instruction streams on a single processor (simultaneous multithreading or SMT), or both. This approach is referred to as task parallelism, and it is well supported by the main programming languages, compilers and operating systems. This approach fits naturally with the architecture of a GNSS receiver, which has many channels (one per satellite and frequency band) operating in parallel over the same input data. When programmed with the appropriate design, execution can be accelerated almost linearly with the number of processing cores. However, the spreading of processing tasks along different threads must be carefully designed in order to avoid bottlenecks (either in the processing or in memory access).
In combination with task parallelization, software-defined receivers can still resort to another form of parallelization: instructions that can be applied to multiple data elements at the same time, thus exploiting data parallelism. This computer architecture is known as Single Instruction Multiple Data (SIMD), where a single operation is executed in one step on a vector of data, as illustrated in FIGURE 3.
FIGURE 3. Illustration of the operation of single-instruction multiple-data (SIMD) processors, which take a multiple-data input (arguments) and produce multiple results, given a single instruction operated in parallel in a set of processing units (PUs).
In GNSS receivers, this type of instruction can implement operations like multiply-and-accumulate across multiple (16, 32, 64 and so on) samples in a single clock cycle. Intel introduced the first instance of 64-bit SIMD extensions, called MMX, in 1997. Later SIMD extensions, SSE 1 to 4, added multiple 128-bit registers. AMD quickly followed and SIMD is now present in almost all modern processors.
Later, Intel introduced more new instruction sets called Advanced Vector Extensions (AVX) featuring 256-bit registers, new instructions and a new coding scheme. In 2013, AVX-2 expanded most integer commands to 256 bits and by 2016, the introduction of AVX-512 provided 512-bit extensions. SIMD technology is also present in embedded systems: NEON technology is a 128-bit SIMD architecture extension for the ARMv7 Cortex-A series processors, providing 32 registers, 64-bits wide (dual view as 16 registers, 128-bits wide), and AArch64 NEON for ARMv8 processors, which provides 32 128-bit registers. In many cases, well written code will be automatically implemented as some combination of these SIMD intrinsics. In other cases, they can be coded explicitly.
Hardware Acceleration. Another possibility for accelerating signal processing is to offload computation-intensive portions of the workload to a device external to the main GPP executing the software. This is the case of graphics processing units (GPUs). Such processor architecture follows another parallel programming model called Single Instruction, Multiple Threads (SIMT). While in SIMD elements of short vectors are processed in parallel, and in SMT instructions of several threads are run in parallel, SIMT is a hybrid between vector processing and hardware threading. Currently, Open Computing Language or OpenCL is the most popular open GPU computing language that supports devices from several manufacturers, while CUDA (originally, Compute Unified Device Architecture) is the dominant proprietary framework specific for Nvidia GPUs. The key idea is to exploit the computation power of both GPP cores and GPU execution units in tandem for better utilization of available computing power. The main constraint in using GPUs is memory bandwidth. If not programmed carefully, most of the time will be spent on transferring data back and forth between the GPP and the GPU, instead of in the actual processing. A possible solution to this is an approach known as zero-copy operations, which consists of a unified address space for the GPP and the GPU that facilitates the passing of pointers between them, thus reducing the memory bandwidth requirements.
Similar benefits can be had by offloading correlation to reconfigurable hardware such as FPGAs. The correlation duties can be offloaded to an FPGA and the loop-closure and navigation engine can remain in the GPP. The FPGA is particularly well suited to the GNSS correlation tasks and can implement dedicated low-resolution (such as 1-4 bit) multiply-and-accumulate blocks, where the equivalent 8-, 16- or 32-bit operations on a GPP would be excessive or inefficient. Early approaches involved an FPGA connected as a peripheral device via Ethernet, Peripheral Component Interconnect Express (PCIe) or a similar bus. However, similar to the GPU, the data transfer quickly becomes a bottleneck. This challenge is addressed by integrating the GPP-FPGA packages. An early example of this approach was the Intel Atom E6x5C package hosting an Altera FPGA. More recent examples are Xilinx’s Zynq 7000 family integrating ARM and FPGA processors in a single encapsulation. These SoCs allow the direct injection of signal samples from the RF front end into the FPGA, greatly reducing the amount of information to be interchanged with the GPP. This approach provides flexibility with regard to how tracking and correlation resources are allocated, allowing configurable architectures according to the targeted signals of interest and application at hand, and enabling the execution of full-featured software-defined receivers in small form factor devices.
THE CLOUD
The ability to manage resources as logical entities instead of as physical, hardwired units dedicated to a given application has materialized in business models such as Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructures as a Service (IaaS). A network of software-defined GNSS receivers executed in the cloud, appears to be the next natural step in this technology trend, in which the GNSS receiver is no longer a physical device but a virtualized function provided as a service (see FIGURE 4).
FIGURE 4. Illustration of the cloud-based GNSS signal-processing paradigm. (Courtesy of SPCOMNAV, Universitat Autònoma de Barcelona)
A virtualized software application is a program that can be executed regardless of the underlying computer platform. This can be achieved by packaging the application and all its software requirements (the operating system, supporting libraries and programs) in a single, self-contained software entity, which can be then run on any platform. An instance of a software-defined GNSS receiver executed in a virtual environment can then be called a virtualized GNSS receiver.
Early virtualization was in the form of full or machine virtualization (virtual machine or VM), which is a software application that emulates the hardware environment and functionality of a physical computer. With VMs, a software component called a hypervisor interfaces between the VM environment and the underlying hardware (CPU), providing the necessary layer of abstraction. A VM can run a full operating system, so conventional software applications (such as a software-defined GNSS receiver) can run within a VM without any required change.
Recently, the use of operating system virtualization or software containers has become more popular as they are often faster and more lightweight than VMs. Instead of a hypervisor, software containers use a daemon that supplements the host kernel, and can therefore be more efficient in making use of the underlying hardware. Examples of these software containers are Docker and Ubuntu Snaps. An example of an open-source software-defined GNSS receiver packaged as a Docker container is available.
Virtualized GNSS receivers bring important benefits in two fields: business-wise, as a technology enabler for new GNSS-based services; and also the use of GNSS SDRs as scientific tools, to ensure reproducibility.
As a service enabler, virtualized GNSS receivers allow for automatic and elastic creation, execution and destruction of application instances as required, and intelligent spread of the running instances across computing resources, regardless of processor architecture, host operating system or physical location. Several solutions are reported in the technical literature, many based on the GNSS snapshot-receiver, in which a short batch of data is sent to the software for position, velocity and time computation. Notable examples of such an approach are Microsoft’s energy-efficient GPS sensing with cloud offloading and the system running on Amazon Web Services. These approaches allow extremely low power consumption to the user equipment, at the expense of limited accuracy (ranging from 10 to 100 meters of error) and high latency. Commercially, Trimble offers Catalyst, a subscription-based GNSS receiver cloud-based service for which the user is charged according to the provided accuracy level, although the exact details are not yet public.
Virtualization technologies also offer a convenient solution for security-related applications (such as GPS M-code and Galileo PRS), since the encryption module remains on the service provider’s premises, and there is no need for a security module in the receiver equipment. This approach may enable the widespread use of restricted/authorized signals by the civilian population.
Finally, virtualization also offers important benefits for science. The flexibility of SDR receivers makes them an ideal tool for scientific experiments, since an implementation released under an open source license would allow a scientist to share a complete description of the processing from raw signal samples to the final research results.
STANDARDIZATION EFFORTS
GNSS signals are generally introduced to the front end through a standard interface, perhaps an SMA, MCX, or U.FL RF connector, and the digitized signals depart through another standard interface, perhaps USB, PCIe, or RJ45. However for a GNSS SDR, this is where the standardization ends. As discussed above, it is clear that there is a wide range of possibilities when capturing and digitizing a GNSS spectrum. Before processing this stream of digitized samples, details such as sample rate, center frequency, sample resolution and format/packing, and a variety of other parameters must be established. This is particularly important in a variety of scenarios such as when sharing/post-processing archived datasets in scientific applications, when offloading computational burden to a cloud-computer, or when interfacing different data-capture devices with different receivers. Ad-hoc methods of digitized data formats do not encourage interoperability and instead cultivate the potential for technology segmentation.
To address this challenge, The Institute of Navigation has lead an effort to develop a specification for standardized metadata, which would accurately and unambiguously describe the digitized data. Adoption of this metadata standard both by the data collection hardware and the software-defined radio receiver can promote interoperability, and can reduce the potential for error. Similarly, an SDR processor’s utility is extended when it is capable of supporting many file formats from multiple sources seamlessly. For more detail on the initiative, readers are encouraged to visit sdr.ion.org.
NEW FRONTIERS: GNSS SDRS IN SPACE
In space, GNSS receivers need to operate in scenarios that are quite different from those of ground-based receivers: higher (albeit predictable) dynamics conditions, low signal-to-noise-density ratios and poor positioning geometry. It is then an excellent scenario for SDRs, since it requires non-standard features from the receiver.
However, space is a harsh environment for semiconductor devices. Charged particles and gamma rays create ionization, which can alter device parameters. In addition to permanently damaging complementary metal-oxide semiconductor (CMOS) ICs, radiation may cause single-event effects, which are caused by ionizing radiation strikes that discharge the charge in storage elements, such as configuration memory cells, user memory and registers. When those effects happen, the system is usually recoverable with a power reset or a memory rewrite, but they also may destroy the device.
Until recently, radiation-hardened solutions were limited to application-specific integrated circuits or ASICs and one-time-programmable solutions. However, recently there has been an increase in the availability of space-grade FPGAs and memory devices. As examples, we can mention Xilinx’s Virtex-5QV, Microsemi’s RTG4 and Atmel’s ATF80 FPGA processors, and commercial SDR platforms such as GOMspace’s GOMX-3. Those devices allow the implementation of space-qualified GNSS receivers fully defined by software.
SDR receivers offer both reprogrammability (or upgradeability) and self-healing (or auto-remediation) capabilities. Examples could be the possibility to upload algorithms yet-to-be-invented at the receiver’s launch time, or the ability to recover from a single-event effect by remotely rewriting damaged functionalities, reducing the need of onboard redundancy.
THE ECONOMICS OF SDRS
Flexibility has a cost—and more flexibility costs more. This is why an FPGA implementation of a complex system can never compete with the unit cost of a fixed function ASIC. An example of a virtuous overlap might be seen in the Maxim 2120 and 2112 line of DVB-S2 TV receiver ICs, which have been successfully co-opted for GNSS SDR front ends due to their features (configurable mixers, gains, filters, operating power range and so on), which happen to be a good-enough match for the GNSS domain. On initial inspection, this allows for flexibility between the two application spaces and provides an ideal platform for SDRs supporting both TV decoding or GNSS on the same hardware radio module, but soon problems appear. The MAX21xx series are designed for TV applications, and TV applications tend to use 75-ohm input impedances while GNSS has standardized on 50 ohms. Certainly, one could add a software-defined impedance-selector block to the design, but we are now spending real hardware resources to accommodate SDR options. Adding an application that requires reception and transmission such as Wi-Fi, adds an entire signal chain to the design, as well as a large increase in the required dynamic range of the system. Adding an application that exploits MIMO, multiplies the hardware resources needed.
The flexibility of SDR makes it an indispensable research, development, validation and hobbyist tool, but system design is about target selection and trade-offs. To quote one of the most successful engineers of the current era and Eckert-Mauchly Award winner Dr. Robert P. Colwell: “Pick your [technical] targets judiciously. … Pick your vision and then chase it. You can’t pick everything as your vision, that’s a recipe for mediocrity. If you can’t pick your target you’re not going to hit any of them.” For SDR-based systems, this would seem to mean that we should focus on applications where the flexibility afforded offsets the inevitable platform cost push, or where it allows targets of opportunity that require a subset of the capabilities of the platform already being used.
At the same time, our earlier definition of an SDR as “a reconfigurable radio system whose characteristics are partially or fully defined via software or firmware” means that SDRs are already everywhere around us on some level. Cellular phones provide an example of devices that connect a large number of hardware radios to a dizzying array of applications that process, consume, modify and sometimes retransmit the received data, while consumer devices such as wireless routers can often add support for protocol changes or tweaks via firmware. While the economics might prevent radio systems from being universal on all dimensions, there are very few radio devices now sold that don’t expose at least a few parameters via software.
CONCLUSION
It seems that we are at an interesting epoch in the evolution of the software-defined GNSS receiver. The GNSS community has begun to springboard off developments and advances in RF equipment and is enjoying both an increase in functionality and a reduction in cost.
Simultaneously, the software-defined GNSS receiver architecture has morphed in multiple directions, enjoying virtually unlimited processing power of cloud computing, or availing itself of fully integrated RF and host-processor modules. As the use cases and host environments for GNSS receivers continue to diversify and the need for flexibility in the receiver continues to increase, it may be that the software-defined GNSS receiver emerges as a contender for the ASIC receiver for certain specialized use cases. Furthermore, as navigation is increasingly provided by an internet-connected device, the software-defined radio may even carve out its own niche, to become the go-to solution.
ACKNOWLEDGMENTS
The authors thank Sanjeev Gunawardena at the Air Force Institute of Technology and José López-Salcedo of Universitat Autònoma de Barcelona for their discussions and correspondence and for providing valuable insight and suggestions.
JAMES T. CURRAN received a Ph.D. in electrical engineering in 2010 from the Department of Electrical Engineering, University College Cork, Ireland. He is a radio-navigation engineer at the European Space Agency in the Netherlands.
CARLES FERNÁNDEZ-PRADES received an M.Sc. and a Ph.D. in electrical engineering from the Universitat Politecnica de Catalunya, Barcelona, Spain, in 2001 and 2006, respectively. In 2006, he joined Centre Tecnològic Telecomunicacions Catalunya, Barcelona, where he holds a position as senior researcher and serves as head of the Communications Systems Division.
AIDEN MORRISON received his Ph.D. in 2010 from the University of Calgary, where he worked on ionospheric phase scintillation characterization using multi-frequency civil GNSS signals. He works as a research scientist at SINTEF Digital in Trondheim, Norway.
MICHELE BAVARO received his master’s degree in computer science from the University of Pisa, Italy, in 2003. After working for several organizations including his own consulting firm, he was appointed as a technical officer at the Joint Research Centre of the European Commission in Brussels. He now works at Swift Navigation in San Francisco, California.
Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory by I.G. Petrovski and T. Tsujii with foreword by R.B. Langley, published by Cambridge University Press, Cambridge, U.K., 2012.
A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by K. Borre, D.M. Akos, N. Bertelsen, P. Rinder, and S.H. Jensen, published by Birkhäuser Engineering, Springer-Verlag GmbH, Heidelberg, 2007.
“Snapshot Positioning for Unaided GPS Software Receivers” by Y. Qian, X. Cui, M. Lu and Z. Feng in Proceedings of ION GNSS 2008, the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 16–19, 2008, pp. 2343-2350.
• Cloud GNSS Signal Processing
“A Cloud Optical Access Network for Virtualized GNSS Receivers” by C. Fernández-Prades, C. Pomar, J. Arribas, J.M. Fàbrega, J. Vilà-Valls, M. Svaluto Moreolo, R. Casellas, R. Martínez, M. Navarro, F.J. Vílchez, R. Muñoz, R. Vilalta, L. Nadal and A. Mayoral in Proceedings of ION GNSS+ 2017, the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 25–29, 2017, pp. 3796–3815.
“Computational Performance of a Cloud GNSS Receiver Using Multi-thread Parallelization” by V. Lucas-Sabola, G. Seco-Granados, J.A. López-Salcedo, J.A. García-Molina, and M. Crisci in Proceedings of Navitec 2016, the 8th Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands, Dec. 14–16, 2016, doi: 10.1109/NAVITEC.2016.7849357.
“CO-GPS: Energy Efficient GPS Sensing with Cloud Offloading” by J. Liu, B. Priyantha, T. Hart, Y. Jin, W. Lee, V. Raghunathan, H.S. Ramos and Q. Wang in IEEE Transactions on Mobile Computing, Vol. 15, No. 6, June 2016, pp. 1348–1361, doi: 10.1109/TMC.2015.2446461.
• High-Performance RF Sampling
“A 13b 4GS/s Digitally Assisted Dynamic 3-stage Asynchronous Pipelined-SAR ADC” by B. Vaz, A. Lynam and B. Verbruggen in Proceedings of 2017 ISSCC, the IEEE International Solid-State Circuits Conference, San Francisco, California, Feb. 5–9, 2017, pp. 276-277, doi: 10.1109/ISSCC.2017.7870368.
In April 2016, I introduced readers to useful features of our newly redesigned website at GPS World. This month, I want to again remind readers of all that we offer — features that may not be apparent if you just visit the homepage to read the news.
In our redesign, we endeavored to make the website even easier to use. Part of that effort consolidated some of our most popular features under the More dropdown menu. The little word appears at the far right of the menu row under our logo. Within it is a world of data and information to explore.
For those seeking current and historical data on the satellites in the various GNSS constellations, we have a full Almanac, which we update at least twice a year for the print magazine.
If you want to stay on top of Upcoming GNSS Satellites Launches, we provide a handy table that is updated frequently by the one and only Richard Langley, our GNSS guru. Richard updates the table frequently — whenever new launch dates are announced.
Richard also oversees the numerous and informative Innovation columns, all of which are available under the Innovation tab — right there under More.
Our most current issue can be accessed through the words Digital Edition at the bottom of the page. Or, again under More, go to Magazine Archive for a full collection of every digital issue that reaches back a decade to 2005.
Other great resources under More are our annual Receiver Survey and Antenna Survey. Both of these products are time intensive to produce, pulling together data and specs from almost 100 companies in an effort to provide a full picture of the products available and their capabilities.
Similarly, the Buyers Guide link will take you to a special section on our website, allowing you to search manufacturers by product category and subcategory. Major updates of the Buyers Guide appear in print in June, but the online Buyers Guide is updated by companies year-round.
If your company isn’t in our Buyers Guide, click on the “Add My Listing” link in the top right corner of the Buyers Guide page. It’s free!
OVER THE YEARS, many philosophers tried to describe the phenomenon of inertia but it was Newton, in his Philosophiæ Naturalis Principia Mathematica, who unified the states of rest and movement in his First Law of Motion. One rendering of this law states: Every body continues in its state of rest, or of uniform motion in a straight line, unless it is compelled to change that state by forces impressed upon it. Newton didn’t actually use the word inertia in describing the phenomenon, but that is how we now refer to it.
In his other two laws of motion, Newton describes how a force (including that of gravity) can accelerate a body. And as we all know, acceleration is the rate of change of velocity, and velocity is the rate of change of position. So, if the acceleration vector of a body can be precisely measured, then a double integration of it can provide an estimate of the body’s position. That sounds quite straightforward, but the devil is in the details. Not only do we have to worry about the constants of integration (or the initial conditions of velocity and position), but also the direction of the acceleration vector and its orthogonal components. Nevertheless, the first attempts at mechanizing the equations of motion to produce what we call an inertial measurement unit or IMU were made before and during World War II to guide rockets.
Nowadays, IMUs typically consist of three orthogonal accelerometers and three orthogonal rate-gyroscopes to provide the position and orientation of the body to which it is attached. And ever since the first units were developed, scientists and engineers have worked to miniaturize them. We now have micro-electro-mechanical systems (or MEMS) versions of them so small that they can be housed in small packages with dimensions of a few centimeters or embedded in other devices.
One problem with IMUs, and with the less-costly MEMS IMUs in particular, is that they have biases that grow with time. One way to limit these biases is to periodically use another technique, such as GNSS, to ameliorate their effects. But what if GNSS is unavailable? Well, in this month’s column we take a look at an ingenious technique that makes use of how the human body works to develop an accurate pedestrian navigation system — one whose accuracy has been checked using drone imagery. As they might say in New York, “Hey, I’m walking (with accuracy) here!”
Satellite navigation systems have achieved great success in personal positioning applications.
Nowadays, GNSS is an essential tool for outdoor navigation, but locating a user’s position in degraded and denied indoor environments is still a challenging task. During the past decade, methodologies have been proposed based on inertial sensors for determining a person’s location to solve this problem.
One such solution is a personal pedestrian dead-reckoning (PDR) system, which helps in obtaining a seamless indoor/outdoor position. Built-in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes. PDR positioning solutions find many applications in security monitoring, personal services, navigation in shopping centers and hospitals and for guiding blind pedestrians.
Several dead-reckoning navigation algorithms for use with inertial measurement units (IMUs) have been proposed. However, these solutions are very sensitive to the alignment of the sensor units, the inherent instrumental errors, and disturbances from the ambient environment — problems that cause accuracy to decrease over time. In such situations, additional sensors are often used together with an IMU, such as ZigBee radio beacons with position estimated from received signal strength.
In this article, we present a PDR indoor positioning system we designed, tested and analyzed. It is based on the pace detection of a foot-mounted IMU, with the use of extended Kalman filter (EKF) algorithms to estimate the errors accumulated by the sensors.
PDR DESIGN AND POSITIONING METHOD
Our plan in designing a pedestrian positioning system was to use a high-rate IMU device strapped onto the pedestrian’s shoe together with an EKF-based framework. The main idea of this project was to use filtering algorithms to estimate the errors (biases) accumulated by the IMU sensors. The EKF is updated with velocity and angular rate measurements by zero-velocity updates (ZUPTs) and zero-angular-rate updates (ZARUs) separately detected when the pedestrian’s foot is on the ground. Then, the sensor biases are compensated with the estimated errors.
Therefore, the frequent use of ZUPT and ZARU measurements consistently bounds many of the errors and, as a result, even relatively low-cost sensors can provide useful navigation performance. The PDR framework, developed in a Matlab environment, consists of five algorithms:
Initial alignment that calculates the initial attitude with the static data of accelerometers and magnetometers during the first few minutes.
IMU mechanization algorithm to compute the navigation parameters (position, velocity and attitude).
Pace detection algorithm to determine when the foot is on the ground; that is, when the velocity and angular rates of the IMU are zero.
ZUPT and ZARU, which feed the EKF with the measured errors when pacing is detected.
EFK estimation of the errors, providing feedback to the IMU mechanization algorithm.
INITIAL ALIGNMENT OF IMU SENSOR
The initial alignment of an IMU sensor is accomplished in two steps: leveling and gyroscope compassing. Leveling refers to getting the roll and pitch using the acceleration, and gyroscope compassing refers to obtaining heading using the angular rate.
However, the bias and noise of gyroscopes are larger than the value of the Earth’s rotation rate for the micro-electro-mechanical system (MEMS) IMU, so the heading has a significant error. In our work, the initial alignment of the MEMS IMU is completed using the static data of accelerometers and magnetometers during the first few minutes, and a method for heading was developed using the magnetometers.
PACE-DETECTION PROCESS
When a person walks, the movement of a foot-mounted IMU can be divided into two phases. The first one is the swing phase, which means the IMU is on the move. The second one is the stance phase, which means the IMU is on the ground. The angular and linear velocity of the foot-mounted IMU must be very close to zero in the stance phase. Therefore, the angular and linear velocity of the IMU can be nulled and provided to the EKF. This is the main idea of the ZUPT and ZARU method.
There are a few algorithms in the literature for step detection based on acceleration and angular rate. In our work, we use a multi-condition algorithm to complete the pace detection by using the outputs of accelerometers and gyroscopes.
As the acceleration of gravity, the magnitude of the acceleration ( |αk| ) for epoch k must be between two thresholds. If
(1)
then, condition 1 is
(2)
with units of meters per second squared. The acceleration variance must also be above a given threshold. With
(3)
where is a mean acceleration value at time k, and s is the size of the averaging window (typically, s = 15 epochs), the variance is computed by:
. (4)
The second condition, based on the standard deviation of the acceleration, is computed by:
. (5)
The magnitude of the angular rate ( ) given by:
(6)
must be below a given threshold:
. (7)
The three logical conditions must be satisfied at the same time, which means logical ANDs are used to combine the conditions:
C = C1 & C2 & C3. (8)
The final logical result is obtained using a median filter with a neighboring window of 11 samples. A logical 1 denotes the stance phase, which means the instrumented-foot is on the ground.
EXPERIMENTAL RESULTS
The presented method for PDR navigation was tested in both indoor and outdoor environments. For the outdoor experiment (the indoor test is not reported here), three separate tests of normal, fast and slow walking speeds with the IMU attached to a person’s foot (see FIGURE 1) were conducted on the roof of the Institute of Space Science and Technology building at Nanchang University (see FIGURE 2). The IMU was configured to output data at a sampling rate of 100 Hz for each test.
For experimental purposes, the user interface was prepared in a Matlab environment. After collection, the data was processed according to our developed indoor pedestrian dead-reckoning system. The processing steps were as follows: Setting the sampling rate to 100 Hz; setting initial alignment time to 120 seconds; downloading the IMU data and importing the collected data at the same time; selecting the error compensation mode (ZARU + ZUPT as the measured value of the EKF); downloading the actual path with a real measured trajectory with which to compare the results (in the indoor-environment case).
For comparison of the IMU results in an outdoor environment, a professional drone was used (see FIGURE 3) to take a vertical image of the test area (see FIGURE 4). Precise raster rectification of the image was carried out using Softline’s C-GEO v.8 geodetic software. This operation is usually done by loading a raster-image file and entering a minimum of two control points (for a Helmert transformation) or a minimum of three control points (for an affine transformation) on the raster image for which object space coordinates are known. These points are entered into a table. After specifying a point number, appropriate coordinates are fetched from the working set. Next, the points in the raster image corresponding to the entered control points are indicated with a mouse.
FIGURE 3. Professional drone. (Photo: DJI)
For our test, we measured four ground points using a GNSS receiver (marked in black in Figure 4), to be easily recognized on the raster image (when zoomed in). A pre-existing base station on the roof was also used. To compute precise static GPS/GLONASS/BeiDou positions of the four ground points, we used post-processing software. During the GNSS measurements, 16 satellites were visible. After post-processing of the GNSS data, the estimated horizontal standard deviation for all points did not exceed 0.01 meters. The results were transformed to the UTM (zone 50) grid system. For raster rectification, we used the four measured terrain points as control points. After the Helmert transformation process, the final coordinate fitting error was close to 0.02 meters.
For comparing the results of the three different walking-speed experiments, IMU stepping points (floor lamps) were chosen as predetermined route points with known UTM coordinates, which were obtained after raster image rectification in the geodetic software (marked in red in Figure 4).
After synchronization of the IMU (with ZUPT and ZARU) and precise image rectification, positions were determined and are plotted in Figure 4. The trajectory reference distance was 15.1 meters.
PDR positioning results of the slow-walking test with ZARU and ZUPT corrections were compared to the rectified raster-image coordinates. The coordinate differences are presented in FIGURE 5 and TABLE 1.
FIGURE 5. Differences in the coordinates between the IMU slow-walking positioning results and the rectified raster-image results. (Chart: Authors)
Table 1. Summary of coordinate differences between the IMU slow-walking positioning results and the rectified raster-image results. (Data: Authors)
The last two parts of the experiment were carried out to test normal and fast walking speeds. The comparisons of the IMU positioning results to the “true” positions extracted from the calibrated raster image are presented in FIGURES 6 and 7 and TABLES 2 and 3.
FIGURE 6. Differences in the coordinates between the IMU normal-walking positioning results and the rectified raster-image results. (Chart: Authors)FIGURE 7. Differences in the coordinates between the IMU fast-walking positioning results and the rectified raster-image results. (Chart: Authors)Table 2. Summary of coordinate differences between the IMU normal-walking positioning results and the rectified raster-image results. (Data: Authors)Table 3. Summary of coordinate differences between the IMU fast-walking positioning results and the rectified raster-image results. (Data: Authors)
From the presented results, we can observe that the processed data of the 100-Hz IMU device provides a decimeter-level of accuracy for all cases. The best results were achieved with a normal walking speed, where the positioning error did not exceed 0.16 meters (standard deviation). It appears that the sampling rate of 100 Hz makes the system more responsive to the authenticity of the gait.
However, we are aware that the test trajectory was short, and that, due to the inherent drift errors of accelerometers and gyroscopes, the velocity and positions obtained by these sensors may be reliable only for a short period of time. To solve this problem, we are considering additional IMU position updating methods, especially for indoor environments.
CONCLUSIONS
We have presented results of our inertial-based pedestrian navigation system (or PDR) using an IMU sensor strapped onto a person’s foot. An EKF was applied and updated with velocity and angular rate measurements from ZUPT and ZARU solutions.
After comparing the ZUPT and ZARU combined final results to the coordinates obtained after raster-image rectification using a four-control-point Helmert transformation, the PDR positioning results showed that the accuracy error of normal walking did not exceed 0.16 meters (at the one-standard-deviation level). In the case of fast and slow walking, the errors did not exceed 0.20 meters and 0.32 meters (both at the one-standard-deviation level), respectively (see Table 4 for combined results).
Table 4. Summary of coordinate differences between the IMU slow-, normal- and fast-walking positioning results and the rectified raster-image results. (Data: Authors)
The three sets of experimental results showed that the proposed ZUPT and ZARU combination is suitable for pace detection; this approach helps to calculate precise position and distance traveled, and estimate accumulated sensor error.
It is evident that the inherent drift errors of accelerometers and gyroscopes, and the velocity and position obtained by these sensors, may only be reliable for a short period of time. To solve this problem, we are considering additional IMU position-updating methods, especially in indoor environments. Our work is now focused on obtaining absolute positioning updates with other methods, such as ZigBee, radio-frequency identification, Wi-Fi and image-based systems.
ACKNOWLEDGMENTS
The work reported in this article was supported by the National Key Technologies R&D Program and the National Natural Science Foundation of China. Thanks to NovAtel for providing the latest test version of its post-processing software for the purposes of this experiment. Special thanks also to students from the Navigation Group of the Institute of Space Science and Technology at Nanchang University and to Yuhao Wang for his support of drone surveying.
MANUFACTURERS
The high-rate IMU used in our work was an Xsense MTi miniature MEMS-based Attitude Heading Reference System. We also used NovAtel’s Waypoint GrafNav v. 8.60 post-processing software and a DJI Phantom 3 drone.
MARCIN URADZIŃSKI received his Ph.D. from the Faculty of Geodesy, Geospatial and Civil Engineering of the University of Warmia and Mazury (UWM), Olsztyn, Poland, with emphasis on satellite positioning and navigation. He is an assistant professor at UWM and presently is a visiting professor at Nanchang University, China. His interests include satellite positioning, multi-sensor integrated navigation and indoor radio navigation systems.
HANG GUO received his Ph.D. in geomatics and geodesy from Wuhan University, China, with emphasis on navigation. He is a professor of the Academy of Space Technology at Nanchang University. His interests include indoor positioning, multi-sensor integrated navigation systems and GNSS meteorology. As the corresponding author for this article, he may be reached at [email protected].
CLIFFORD MUGNIER received his B.A. in geography and mathematics from Northwestern State University, Natchitoches, Louisiana, in 1967. He is a fellow of the American Society for Photogrammetry and Remote Sensing and is past national director of the Photogrammetric Applications Division. He is the chief of geodesy in the Department of Civil and Environmental Engineering at Louisiana State University, Baton Rouge. His research is primarily on the geodesy of subsidence in Louisiana and the grids and datums of the world.
FURTHER READING
• Authors’ Work on Indoor Pedestrian Navigation
“Indoor Positioning Based on Foot-mounted IMU” by H. Guo, M. Uradziński, H. Yin and M. Yu in Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 63, No. 3, Sept. 2015, pp. 629–634, doi: 10.1515/bpasts-2015-0074.
“Usefulness of Nonlinear Interpolation and Particle Filter in Zigbee Indoor Positioning” by X. Zhang, H. Guo, H. Wu and M. Uradziński in Geodesy and Cartography, Vol. 63, No. 2, 2014, pp. 219–233, doi: 10.2478/geocart-2014-0016.
• IMU Pedestrian Navigation
“Pedestrian Tracking Using Inertial Sensors” by R. Feliz Alonso, E. Zalama Casanova and J.G. Gómez Garcia-Bermejo in Journal of Physical Agents, Vol. 3, No. 1, Jan. 2009, pp. 35–43, doi: 10.14198/JoPha.2009.3.1.05.
“Pedestrian Tracking with Shoe-Mounted Inertial Sensors” by E. Foxlin in IEEE Computer Graphics and Applications, Vol. 25, No. 6, Nov./Dec. 2005, pp. 38–46, doi: 10.1109/MCG.2005.140.
• Pedestrian Navigation with IMUs and Other Sensors
“Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors” by P.D. Duong, and Y.S. Suh in Sensors, Vol. 15, No. 7, 2015, pp. 15888–15902, doi: 10.3390/s150715888.
“Enhancing Indoor Inertial Pedestrian Navigation Using a Shoe-Worn Marker” by M. Placer and S. Kovačič in Sensors, Vol. 13, No. 8, 2013, pp. 9836–9859, doi: 10.3390/s130809836.
“Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning” by Z. He, V. Renaudin, M.G. Petovello and G. Lachapelle in Sensors, Vol. 13, No. 4, 2013, pp. 4303–4326, doi: 10.3390/s130404303.
“Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements” by A. Ramón Jiménez Ruiz, F. Seco Granja, J. Carlos Prieto Honorato and J. I. Guevara Rosas in IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 1, Jan. 2012, pp. 178–189, doi: 10.1109/TIM.2011.2159317.
• Pedestrian Navigation with Kalman Filter Framework
“Indoor Pedestrian Navigation Using an INS/EKF Framework for Yaw Drift Reduction and a Foot-mounted IMU” by A.R. Jiménez, F. Seco, J.C. Prieto and J. Guevara in Proceedings of WPNC’10, the 7th Workshop on Positioning, Navigation and Communication held in Dresden, Germany, March 11–12, 2010, doi: 10.1109/WPNC.2010.5649300.
“A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors” by Z. Xu, J. Wei, B. Zhang and W. Yang in Sensors Vol. 15, No. 4, 2015, pp. 7708–7727, doi: 10.3390/s150407708.
This year, the Munich Satellite Navigation Summit features an interactive session on the topic “Industry Meets Research: Innovation Drivers and Barriers in SMEs.” Fabio Dovis from Politecnico di Torino will chair the session, and GPS World magazine Publisher and Editor-in-Chief Alan Cameron will moderate the discussion.
“Small and medium-sized enterprises (SMEs) and their innovative ideas are an important factor of economic growth,” states the conference program. “Therefore it is important to improve the environment in which innovative business ideas can be created. A main factor is the promotion and facilitation of technology transfer, thus the access to scientific results. In order to enable a dynamic and creative GNSS product, service and application development, a stronger and more structured link between the most promising results of GNSS research and companies should be fostered.”
Enter the Fishbowl
This session will be organized according to the so-called fishbowl method that will involve GNSS experts from universities, research centers and industry in an interactive discussion. Everybody is welcome to join the fishbowl and to be part of the GNSS Knowledge Triangle to strengthen the knowledge transfer and the future success of GNSS.
According to the fishbowl method, five chairs will be arranged in circles and one chair is always unoccupied. Any member of the audience can, at any time, occupy the empty chair and join the fishbowl. When this happens, an existing member of the fishbowl must voluntarily leave the fishbowl and free a chair. The discussion continues with participants frequently entering and leaving the fishbowl.
Real-time single-frequency precise point positioning for cars and trains
By Peter de Bakker and Christian Tiberius
INNOVATION INSIGHTS with Richard Langley
“IT’S GETTING BETTER ALL THE TIME.”This refrain from the Beatle’s song could well describe precise point positioning or PPP. PPP is a positioning technique that relies on GNSS carrier-phase measurements (in addition to code or pseudorange measurements) from a user’s receiver along with satellite orbit and clock data much more precise (and accurate) than that included in broadcast satellite navigation messages to achieve accuracies down to the centimeter level. It also requires a more sophisticated model of the measurements compared to that used in most consumer GNSS equipment and even some professional devices, including accounting for residual tropospheric propagation delay, carrier-phase windup, and even solid Earth tides.
PPP has been around for more than a decade and ongoing research has gradually improved its capabilities. Until recently, it has been used primarily with dual-frequency GPS observables. However, the technique is not restricted to GPS. It works equally well with observables from other constellations including GLONASS, Galileo and BeiDou. As long as precise orbit and clock products are available (typically from the International GNSS Service or its participating analysis centers), then PPP positioning solutions are possible. And, single-frequency PPP is also possible. The primary advantage of dual-frequency PPP is that the ionospheric propagation delay is almost completely removed by linearly combining the measurements on the two frequencies, taking advantage of the dispersive nature of signal propagation through the ionosphere. But, if good predictions of the ionospheric delay at, say, the L1 GPS frequency are available, then it is possible to do single-frequency PPP. While not as accurate as dual-frequency PPP, the technique is considerably more accurate than typical pseudorange point positioning (the so-called Standard Positioning Service).
PPP is also traditionally a post-processing technique. That is, data is collected but it is not processed until some later convenient time when the necessary precise products are available. Such an approach is useful for many applications but clearly not for navigation, which requires real-time positioning. But in the past few years, a number of commercial and non-commercial entities have started streaming real-time satellite orbit and clock corrections over the Internet and various radio links, making real-time PPP a reality.
In this month’s Innovation column, we bring together, perhaps for the first time, single-frequency and real-time PPP. Our authors describe a series of experiments they have conducted on roadways and a railway achieving sub-meter horizontal positioning at a 95 percent confidence interval. Such accuracies may already be sufficient for freeway lane and railway track guidance. But we might expect even better accuracies in the future. After all, PPP is getting better all the time.
The single-frequency precise point positioning (SF-PPP) method, developed at Delft University of Technology, was previously demonstrated to provide lane-level position accuracy on a freeway in post-processing mode. Important applications of SF-PPP are lane-level traffic state estimation and lane-level specific driver advice for next-generation car navigation. For a functional system, as well as for advanced experiments in this field, the computed positions have to be available in real time. Therefore, a new real-time implementation of the SF-PPP method was developed as part of the Dutch Dynamic Lane Guidance project. In this article, we outline aspects of the real-time implementation, and we present experimental results from this new implementation collected on a busy freeway in the Netherlands and in a parking lot, as well as results from a railway experiment.
In these experiments, a test vehicle was equipped with a low-end, automotive-type single-frequency receiver with a patch antenna to collect raw GPS observations. A 3G mobile communications link was used to obtain data-correction streams over the Internet using the Ntrip protocol. The SF-PPP processing was performed on a laptop computer onboard the vehicle, in real time. Various forms of ground-truth positions were used to assess the real-time SF-PPP positioning accuracy. For some of our tests, the vehicle was also equipped with high-end GPS antennas and receivers to provide ground truth. The position solutions obtained with the SF-PPP algorithm have been compared to (post-processed) network-RTK solutions using the Netherlands Positioning Service (NETPOS). Additional validation was performed by means of a 5-centimeter-accuracy road-infrastructure map from Rijkswaterstaat, the Dutch Ministry of Infrastructure and the Environment, and by a centimeter-level a priori ground survey.
The new real-time SF-PPP software was tested successfully with performance comparable to our previous post-processing software, and meeting the required accuracy for freeway lane identification. Statistics on the performance are provided, as well as their dependence on a number of external parameters including the number of available satellites.
Precise corrections from both the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt or DLR) and the International GNSS Service (IGS) were used. Delays in the correction streams vary between providers and can increase further in the event of a time-out of the mobile link. The influence of these delays is considered, and an optimal approach for dealing with outages is discussed.
PPP Model and Corrections
The GNSS positioning model is non-linear. The observations are non-linear functions of the unknown parameters plus noise.
To solve for the unknown parameters (including the receiver position coordinates), through least squares estimation, the model must be linearized around an approximate solution.
In our SF-PPP model, the primary observations are, from each satellite, the pseudorange measurement and the carrier-phase measurement. The unknown parameters are the receiver position vector and the receiver clock offset, both of which are involved in the linearization, and also the ambiguity, associated with the carrier-phase measurement, for which the model is already linear.
In the context of PPP, it is important to note that in addition to the linearization around the initial approximate values, the computed observations contain a number of a priori model values for parameters which are not estimated, including:
The precise satellite position and clock offset (including the relativistic effect): The GPS satellite positions and clock offsets are computed from the broadcast products (navigation message) and corrected with real-time data streams via Ntrip. The correction streams of DLR and IGS were used at different times as detailed in Table 1. In post-processing older files, the satellite orbits and clocks are taken from sp3 files, but to keep the processing as close as possible to the real-time functionality, these are first converted to corrections to the broadcast products.
The (neutral) troposphere delay: The troposphere delay is modeled with the a priori Saastamoinen model using the Ifadis mapping function and parameters from the 1976 U.S. Standard Atmosphere.
The ionosphere delay and satellite differential code bias: The ionosphere delay is computed a priori using the one-day predicted Global Ionosphere Maps (GIMs) from the Center for Orbit Determination in Europe (CODE), together with the corresponding differential code biases.
The carrier-phase observations are corrected for the phase wind-up at the receiver and satellite. The user orientation is estimated from the vehicle velocity vector.
TABLE 1. Four SF-PPP field tests.
Besides the primary observations, the ambiguity estimate from the previous epoch can be added to the current epoch as an additional observation per satellite, because it is assumed to be constant in the absence of a cycle slip.
Observations from different epochs are assumed to be uncorrelated, and consequently the ambiguity estimates from previous epochs are uncorrelated to the current observations. Observations to different satellites are also assumed to be uncorrelated.
The carrier-phase ambiguities are the only parameters propagated from a previous epoch to the current epoch. The receiver position coordinates (and receiver clock offset) are estimated each epoch anew — no vehicle dynamics model is involved.
The computed positions are finally corrected for solid Earth tides with an efficient numerical model. Computed positions result in the International Terrestrial Reference Frame (ITRF) 2008 at the epoch of the observations.
In parallel with the positioning filter, statistical hypothesis testing is used to detect errors in the observations or propagated ambiguities (such as those caused by excessive multipath or a cycle slip), based on the detection, identification and adaptation (DIA) procedure. First, an overall model test is run at each epoch to test the validity of the model and observations. If the test is rejected, data snooping is applied to determine which observation is most likely to have caused the problem. If one of the pseudorange measurements is identified, it is removed from the model. If either a carrier-phase measurement or ambiguity is identified, the ambiguity for that satellite is reset; that is, the propagated ambiguity is removed.
Experiments
Four field tests that we have carried out are considered here.
In October 2012, more than 100 laps were driven over a 5-kilometer stretch of the A13 freeway between Delft and Rotterdam. The data collected were reprocessed to validate the new real-time software implementation (but obviously carried out in post-processing mode).
The first real-time tests were performed in December 2014 and later in May 2015 on the same stretch of the A13 freeway.
In May 2015, a third dataset was collected on a recently constructed and nicely outlined parking lot in Delft.
In July 2015, a train carriage was equipped with a GPS receiver and data were collected on a train trip from the center of The Netherlands to the far southern part — a distance of more than 200 kilometers.
Details of the four field tests are collected in Table 1.
Ground Truth
In our earlier experiments, the ground truth for the vehicle positions was computed with measurements from high-end equipment onboard the same vehicle. Both the antenna of the SF-PPP receiver and the high-end antennas were rigidly connected to a wooden beam on the roof rack of the van (positions of the two high-end antennas at both ends of the beam were obtained through network RTK GPS). As our results from this experiment show, the performance, and especially the precision, is very good, but a moderate bias of 17 centimeters in the cross-track direction was observed (see FIGURE 1 and TABLE 2). The suspect cause of this bias was the antenna location, close to the side of the vehicle and not attached to the metal roof itself.
FIGURE 1. 2D histogram of SF-PPP position errors (with respect to the network RTK GPS solution) in horizontal directions for the 2012 test on the A13 freeway, expressed in local east and north directions (left), and in cross-track and along-track directions (right). The color indicates the number of samples in each bin.TABLE 2. Statistics of the position errors in each direction, for the 100 laps on the A13 freeway.
Therefore, during more recent experiments, the test vehicle was only equipped with a patch antenna for the low-end, automotive-type GPS receiver, and attached directly to the roof of the car, in the middle of the centerline of the vehicle. In this case, the metal roof acts as a ground plane for the antenna, improving the gain and not acting as a source of multipath. However, this setup also has complications for the accuracy assessment. Thus, instead of computing accurate ground truth from the measurements from high-end equipment directly near the test receiver, a number of other ways were used to determine the ground truth.
During the first real-time test on the A13 freeway, a 5-centimeter accurate road infrastructure map from Rijkswaterstaat was used as previously mentioned. This comparison was done both visually and numerically.
For our next experiment, we selected a recently constructed parking lot with a simple, neat rectangular layout. By surveying the corners of the rectangle and using the repetitive pattern, a schematic drawing of the parking lot was made, and used to evaluate the positioning performance in a visual manner. The car was first driven over the lined-up parking spaces in a lengthwise manner, circling round at each end of the parking lot, and changing lanes once each lap at the same point. Then the car was driven along the edges of the rows of parking spaces to and fro over the parking lot.
SF-PPP positions were obtained live in the vehicle while driving. The raw (single-frequency) observations of this experiment were also post-processed with the RTKLib software package using the nearby permanent DLF1 station at the TU Delft GNSS observatory on a very short baseline (less than 1 kilometer). The ambiguity-fixed results could then be used to also numerically assess the SF-PPP positioning performance.
For the test on the train, again the network RTK GPS solution provided the ground truth positions. Two antennas were mounted along the centerline of the carriage at a fixed offset from each other: a patch antenna for the single-frequencyreceiver and a geodetic antenna for the ground truth. With this known offset, and the direction of motion, the ground truth position for the single-frequency receiver was obtained.
The ground-truth positions, either in the European Terrestrial Reference System (ETRS) 89 (from NETPOS or our own survey) or in the local national reference frame Rijksdriehoeksmeting (National Triangulation System) / Normaal Amsterdams Peil (Amsterdam Ordnance Datum) or RD/NAP, have been transformed into ITRF2008, to allow for comparison with the SF-PPP positions.
Computational Performance, Data Rates
The real-time software was used under the 64-bit Windows 8.1 operating system on a moderately fast laptop with i5-4200U CPU running at 1.60 GHz. The software consists of uncompiled Matlab R2014b scripts and functions using timer objects to repeatedly read in new observations, corrections and ephemerides, and to update the position computation. The software can run with data arriving at about 20 Hz in the current state on this platform, but was used with 5-Hz data because of limitations of the receiver to provide raw data and to prevent any overrun. It should be noted that only a few obvious potential computational bottlenecks were targeted; the software was not optimized for efficiency.
The RT SF-PPP implementation relies on a 3G mobile Internet connection for a number of data products. The ionosphere map, which is a predicted product (24 hours ahead), comes as a 200-kilobyte file (and 5 kilobytes for the associated differential code biases), which covers the globe and is valid for 24 hours. The file contains 13 maps at 2-hour intervals, between which interpolation in time is required.
Spatial interpolation is also required for the ionosphere pierce point of each satellite signal, between the grid points in the map (at intervals of 5 degrees in longitude and 2.5 degrees in latitude). The satellite orbit/position corrections (every 60 seconds) and satellite clock corrections (every 10 seconds) are retrieved over the Internet using the Ntrip protocol by means of the Bundesamt für Kartographie und Geodäsie (BKG) Ntrip Client (BNC), which passes these on to Matlab.
The data-rate used by this correction stream is about 1 kilobit per second. The corrections are applied to the broadcast ephemerides (in quasi-Keplerian-element form), which are therefore also required. These satellite ephemerides can be extracted by the GPS receiver itself (from the GPS navigation message), but in our implementation are also collected via Ntrip for convenience only, with a bandwidth consumption of 6 kilobits per second. Note that, much like the software implementation itself, the data stream has not been optimized for any particular bandwidth limitation. For instance, orbit and clock corrections are needed only for those satellites in view, and hence transmitting the data for all satellites of the constellation is not needed.
Results
In this section, we present the results of our tests, followed in the next section with a discussion of important common factors affecting accuracy and continuity of RT SF-PPP.
Road-Test A13 Freeway (100 Laps). Under different conditions, we collected a large amount of data with a van, driving repeatedly the same 5-kilometer stretch of road on the A13 freeway from Rotterdam to Delft. The test amounted to almost a full day of driving.
2D histograms of the results are shown in Figure 1 with corresponding statistics in TABLE 2. Note a small bias in the cross-track direction. The total number of position solutions was 2.0× 105.
Road-Test A13 Freeway (Real Time). The results of the real-time freeway road test are shown in FIGURE 2. The different lanes used by the vehicle are clearly visible in the figure. The number of GPS satellites is indicated by the color bar. Shown is the Delft-Zuid / TU Delft exit of the A13 freeway, roughly a 300 × 300 meter area, taken from the Digitaal Topografisch Bestand (DTB) of Rijkswaterstaat. Note that only the cross-track performance can be assessed in this manner, but fortunately this is exactly the performance aspect that is most interesting for the target application of lane identification. Note also that if the vehicle was not driving exactly in the middle of the lane, which to some extent is unavoidable, this effect cannot be separated from the positioning errors.
FIGURE 2. SF-PPP solution displayed on a 5-centimeter accurate road infrastructure map, on Dec. 18, 2014.
The 95-percent error southbound and northbound is 0.65 meters and 0.58 meters respectively, in the cross-track direction.
Road-Test Parking Lot.FIGURE 3 shows an aerial photograph (left) and schematic drawing (right) of the 3M company parking lot in Delft showing measured positions and driven tracks. The lines in red and yellow represent the measured tracks while driving the same loop over the parking lot again and again (more than 60 times in total), and the purple lines show the track while driving around and following the parking space boundaries with the left front wheel of the test vehicle (4 laps). These lines show both the SF-PPP position error and the driver error. The white parking spaces are each 2 meters wide.
FIGURE 3. Aerial photograph, from Google Earth, (left) and schematic drawing (right) of the parking lot in Delft showing measured positions and driven tracks.
The position errors in local north, east and up directions for part of the first dynamic session, of about 4.5 laps, of the 3M parking lot experiment (lane change 1) are shown in the upper panel of FIGURE 4. We see a clear periodic signal as well as a bias in each direction. The driving direction gives an approximation of the heading (shown in the bottom panel), which confirms that the periodic signal coincides with the driven laps.
FIGURE 4. Position errors (top) in local north, east and up directions and heading (bottom) for part of the first dynamic session, about 4.5 laps, of the 3M parking lot experiment (lane change 1).
The figure shows that the errors in the position solution are on the order of 0.2 meters, and consist of a bias in each of the three directions and a periodic signal with a period equal to the lap-time (confirmed by the driving direction of the vehicle). Since the bias does not depend on the orientation of the vehicle, and given the slow variation over time, the most likely cause is a residual ionosphere error or errors in the satellite products. The repeating pattern, on the other hand, is most probably related to multipath or near-field effects related to the vehicle antenna.
Rail-Test Amersfoort to Simpelveld. The train carriage with the GPS antennas installed was pulled by a 1955-built diesel-electric locomotive. A trip of more than 200 kilometers was made, over the main Intercity Network of Nederlandse Spoorwegen (NS) / ProRail (Dutch Railways). Only the last 20 kilometers were on a local line to a historic railway station.
The overhead power line (about 1 meter above the GPS antennas) and portals seem to have no impact on the SF-PPP positioning performance. An example of the positioning accuracy is shown in FIGURE 5. The figure shows position error scatter for an almost 20-kilometer stretch of nearly straight east-west track through rural and forest areas (Weert to Roermond). The time span of the data is 10 minutes, and the data rate was 5 Hz. SF-PPP positions were compared with NETPOS network RTK GPS solutions. Generally, eight satellites were received and used in the SF-PPP solution. The corresponding error statistics are presented in TABLE 3.
FIGURE 5. Position error scatter for an almost 20-kilometer stretch of nearly straight east-west track through rural and forest areas (Weert to Roermond); 10 minutes of data at 5 Hz.TABLE 3. Statistics of the position errors, over 2994 epochs, in along- and cross-track directions, for the position scatter shown in Figure 5.
A heavy steel-construction bridge along the route at the River Lek near Culemborg, 15 kilometers south of Utrecht, was found to degrade positioning performance considerably. The heavy steel construction of the bridge hampers reception of GPS satellite signals. The positioning performance on the bridge is shown in FIGURE 6. The computed SF-PPP trajectory overlaid on a Google Earth aerial photograph is shown on the left.
FIGURE 6. Positioning performance on the Lek Bridge. Left: measured trajectory overlaid on a Google Earth aerial photograph. The number of satellites available is indicated by the color bar. Right top: SF-PPP positions in local east-north directions. Right bottom: Absolute cross-track offset of position solution with respect to a straight line, as a function of time.
From the positions, one can clearly see the train driving straight on the right-hand track (going south) on the ramp onto the bridge, and on the ramp down from the bridge. However, on the bridge itself, position solutions show considerably larger variations of up to 8 meters. The image shows a 250-meter stretch of the track. Also, the number of satellites available, and used in the position solution, drops considerably (indicated by the color bar) while the train is on the bridge. On the right of the figure at the top, the SF-PPP positions in local east-north coordinates are shown along with a straight line between the first and last epochs, representing the assumed straight track. The plot at bottom right shows the absolute cross-track offset of the position solutions with respect to the straight line, as a function of time, over 250 5-Hz epochs.
Analysis
Two factors significantly affect the performance of our tests: the number of satellites available and the continuity and latency of the corrections.
Number of Satellites.As can be expected, the SF-PPP position accuracy depends to a large extent on the number of satellites used to compute the solution. For the third test, the road-test in the 3M parking lot, the three-dimensional position error (SF-PPP versus RTK GPS) is shown as a boxplot in FIGURE 7 in which various accuracy measures are plotted as a function of the number of satellites for the second and longest dynamic part of the test (lane change 2), consisting of about 12,000 epochs of data. During this session, the available number of satellites varied between 10 and 12. This number was reduced artificially by increasing the elevation mask angle to 15 and to 30 degrees. The red lines show the medians, the boxes show the 25th and 75th percentiles, the dashed lines cover all data points not considered outliers, and outliers are plotted with red plus signs. The graph shows a clear improvement going from six to seven or more satellites.
FIGURE 7. Boxplot of 3D position error vs. the number of satellites for the second and longest dynamic part of the 3M parking lot test (lane change 2).
PPP Correction-Stream Outages.To determine the optimal approach to an interruption in the correction data stream, we studied the variation of the corrections over time. Suppose we lose reception of the correction stream at epoch 0, and we keep using the last-received corrections (simply hold onto them). Then the change in values can be interpreted as the additional error introduced in the positioning algorithm by the outage on the mobile link. The effect is not catastrophic. Only after about 200 seconds do the additional satellite clock errors grow to the decimeter level. The position errors remain even smaller.
However, one might wonder whether this can be improved further by performing a linear extrapolation of the corrections, for example, using a number of previous epochs. We looked at what would happen in this case if 5 minutes of previous data are used. For the clock errors, there is no real benefit — the errors only grow larger. But the position errors do remain smaller during the first 5 minutes of extrapolation. After that time, the errors are larger than those without the linear extrapolation (just holding onto the last corrections). The effect of increasing the order of the polynomial extrapolation was also considered. The polynomials of different order outperform each other at different extrapolation times, and also the number of previous epochs used for the polynomial estimation impacts this. Further optimization to reduce the satellite position errors might well be possible, but may be of marginal value, since, the extrapolated clock error is dominant and polynomial extrapolation does not improve this. Simply using the most recent corrections is thus a straightforward and acceptable approach.
Conclusions
In this article, we outlined a real-time implementation of single-frequency GPS precise point positioning. With a fairly low-cost GPS receiver and reception of a modest correction data stream, it is possible to achieve sub-meter horizontal positioning accuracy, in real-time, live in the vehicle (95-percent error of better than 1 meter). Actual results were shown from four field tests: two tests using a vehicle on a freeway, a vehicle test in a parking lot, and one test on a train.
The number of satellites used in the position solution has a big effect on the positioning performance; seven or more satellites yields a good position accuracy. And up to 5 minutes outage of the satellite position and clock corrections does not seem to pose a serious threat to SF-PPP positioning performance.
Acknowledgments
The Dynamic Lane Guidance project under which the first road test was carried out was funded by the Ministry of Infrastructure and Environment, the Province of Noord-Brabant and the Eindhoven Regional Government in the context of Brabant in-car III. This project was carried out in close cooperation with colleagues in the Transport and Planning Department at TU Delft.
We acknowledge the provision of the Real-Time Clock Estimation (RETICLE) satellite clock products by André Hauschild at DLR for several of our field tests. We are also grateful for the use of the IGS Real-Time Service. Also, we acknowledge the provision of the NETPOS network RTK GPS service as ground truth by Lennard Huisman of Kadaster, the Dutch Land Registry and Mapping Agency. Colleague Hans van der Marel analyzed the NETPOS RTK-GPS solution of the train test. Colleagues of the TU Delft Railway Engineering Department offered the opportunity to carry out the test on the train trip from Amersfoort to Simpelveld.
Manufacturers
The vehicle receivers used for the tests were u-blox AG TIM LP and 7P modules in evaluation kits fed by a Tri-M Technologies Inc. Big Brother SM-66 or Taoglas Dominator AA.161 antenna. A Trimble Navigation R7 receiver with a Zephyr Geodetic antenna was used to establish ground truth for some tests.
PETER DE BAKKER is a researcher in the Faculty of Civil Engineering and Geosciences at Delft University of Technology (TU Delft). He recently finished his Ph.D. dissertation on user algorithms for GNSS precise point positioning, and is working on localization for automotive applications, including autonomous vehicles.
CHRISTIAN TIBERIUS is an associate professor in the Faculty of Civil Engineering and Geosciences at TU Delft. He has been involved in GNSS positioning and navigation research since 1991, currently with an emphasis on data quality control, satellite-based augmentation and precise point positioning.
Further Reading
• Earlier Work on Single-Frequency Precise Point Positioning
“Lane Identification with Real Time Single Frequency Precise Point Positioning: A Kinematic Trial” by R.J.P. Van Bree, P.J. Buist, C.C.J.M. Tiberius, B. van Arem and V.L. Knoop in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation Portland, Ore., Sept. 19–23, 2011, pp. 314–323.
“Real Time Satellite Clocks in Single Frequency Precise Point Positioning” by R.J.P. Van Bree, C.C.J.M. Tiberius and A. Hauschild in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Ga., Sept. 22–25, 2009, pp. 2400–2414.
“Single-frequency Precise Point Positioning with Optimal Filtering” by A.Q. Le and C. C. J. M. Tiberius in GPS Solutions, Vol. 11, No. 1, 2007, pp. 61–69, doi: 10.1007/s10291-006-0033-9.
• Single- vs. Dual-Frequency Precise Point Positioning
Improved Convergence for GNSS Precise Point Positioning by S. Banville, Ph.D. dissertation, Department of Geodesy and Geomatics Engineering, Technical Report No. 294, University of New Brunswick, Fredericton, New Brunswick, Canada. Recipient of The Institute of Navigation 2014 Bradford W. Parkinson Award.
“Variations of Box Plots” by R. McGill, J.W. Tukey and W.A. Larsen in The American Statistician, Vol. 32, No. 1, Feb. 1978, pp. 12–16, doi: 10.2307/2683468.
A Wide-Area Multi-Application PNT Resiliency Solution
By Stephen Bartlett, Gerard Offermans and Charles Schue
INNOVATION INSIGHTS with Richard Langley
WHERE HAVE ALL THE SYSTEMS GONE, long time passing?
Radionavigation systems, that is (and apologies to Pete Seeger). If we look at the 1990 Federal Radionavigation Plan (FRP), published by the U.S. Departments of Transportation and Defense, as I did in this column in March 1992, we see that there were 10 radionavigation systems in use by different user segments: Loran-C, Omega, very high frequency (VHF) Omnidirectional Range/Distance Measuring Equipment, Tactical Air Navigation, the Instrument Landing System, the Microwave Landing System, Transit, aviation radiobeacons, marine radiobeacons and GPS.
The latest FRP, issued in 2014, includes only seven or six and a half when you consider that marine radiobeacons were mostly phased out in the intervening years. Systems were shut down because with the advent of GPS, they were considered to be redundant. While there were attendant cost savings, the closure of the various systems has resulted in a dangerous virtual sole dependence on GPS for navigation without any backup.
Transit, was the first to go. It consisted of a constellation of six or seven active satellites in circular, polar orbits at altitudes of roughly 1,100 kilometers. The satellites transmitted signals on 150 and 400 MHz, and receivers measured the integrated Doppler frequency shift of the received signals. Transit was terminated at the end of 1996.
Transit was followed by the Omega hyperbolic navigation system. Omega consisted of eight stations around the globe transmitting time-shared carrier-wave signals on four frequencies between 10.2 and 13.6 kHz. The Omega system was closed down in September 1997.
The marine radiobeacons have been mostly shut down in recent years, although aeronautical beacons continue to operate. Radiobeacons are nondirectional transmitters that operate in the low- and medium-frequency bands. Some marine radiobeacons became Differential GPS stations and subsequently part of the Nationwide DGPS network. That network is being scaled back to provide only coastal and Great Lakes coverage.
And that brings us to Loran-C. Like Omega, it was also a hyperbolic navigation system. A receiver measured the difference in times of arrival of pulses transmitted at 100 kHz by a chain of three to five synchronized stations separated by hundreds of kilometers. At one time, the operation of Loran-C was the responsibility of the U.S. Coast Guard. Together with a number of host nations, the Coast Guard operated 17 chains of stations around the world, including one jointly operated with Russia. These stations provided coverage of the coastal areas of North America and the U.S. interior, northern Europe, the Mediterranean Sea, the Far East and the Hawaiian Islands. Additionally, several other countries operated Loran-C stations. Although moves were already underway to update the Loran technology, the Obama administration decided to terminate Loran-C in the U.S., considering it to be an unnecessary antiquated system. The Coast Guard terminated the transmission of all U.S. Loran-C signals in February 2010 and began dismantling stations.
So, is there no longer a viable non-GNSS alternative or backup system for GPS navigation? While there are other possibilities for time transfer, one of GPS’s other applications, there is no widely available substitute navigation system. Currently. However, as we will see in this month’s column, a new version of Loran — Enhanced Loran or eLoran — has been developed and is being tested on the U.S. east coast. Not your father’s Loran, eLoran seems to be the perfect solution for PNT resiliency.
Telecommunications, energy, finance and transportation are just four among the many critical infrastructure / key resource sectors that have come to rely solely on GPS for positioning, navigation and timing (PNT). In fact, the U.S. Department of Homeland Security (DHS) has determined that 11 of the 16 critical infrastructure sectors in the U.S. are critically dependent on GPS for timing. While we can start to imagine what a day without GPS might be like, we’d really rather not — it would be somewhat depressing and really quite dangerous. We would rather imagine a day when there is a wide-area complementary solution available that protects and augments GPS. In this article, we will delve into such a solution: Enhanced Loran, or eLoran for short. We will explain how it works, debunk some myths, speculate on how it could be used in the U.S. (and abroad), highlight the state of current technology and discuss the state of the possible. We will also summarize the state of eLoran in the world and where things might go from here.
What Is eLoran?
eLoran is the latest in the longstanding and proven series of low-frequency, LOng-RAnge Navigation (LORAN) systems, one that takes full advantage of 21st-century technology. It meets the accuracy, availability, integrity and continuity performance requirements for maritime harbor entrance and approach maneuvers, aviation non-precision instrument approaches, land-mobile vehicle navigation and location-based services. It’s a precise source of time (phase) and frequency. Additionally, eLoran provides user bearing (azimuth) and has built-in integrity. In full disclosure, however, eLoran is only a 2D positioning solution unless integrated with a simple altimeter.
eLoran is a low-frequency radionavigation system that operates in the frequency band of 90 to 110 kHz. eLoran is built on internationally standardized Loran-C, and provides a high-power PNT service for use by all modes of transport and in other applications. eLoran is an independent dissimilar complement to GNSS. It allows GNSS users to retain the safety, security and economic benefits of GNSS even when their satellite services are disrupted.
eLoran uses pulsed signals at a center frequency of 100 kHz. The pulses are designed to allow receivers to distinguish between the groundwave and skywave components in the received composite signal. This way, the eLoran signals can be used over very long ranges without fading or uncertainty in the time-of-arrival (TOA) measurement related to skywaves.
Although eLoran is based upon Loran-C, it has key differences:
All transmissions are synchronized to UTC (like GPS)
Time-of-transmission control
The ability to use differential corrections (similar to DGPS)
Receivers use “all-in-view” signals
Includes one or more Loran data channels that provide: Low-rate data messaging, added integrity, differential corrections (dLoran and/or DGPS) and other communications including navigation messages.
An eLoran receiver measures the TOA of the eLoran signal:
TOA = TOR – TOT = PF + SF + ASF + ∆Rx
where TOR is time of reception, TOT is time of transmission, PF is the primary factor (propagation delay through air), SF is the secondary factor (propagation delay over sea), ASF is the additional secondary factor (propagation delay over terrain) and ∆Rx is the delay due to receiver electronics and cables.
The primary and secondary factors are well-defined delays and can be calculated as a function of distance. The additional secondary factor delay is mostly unknown at the time of installation. Fortunately, the ASFs remain very stable over time. Any fine changes in ASF over time may be compensated for by one or more differential eLoran reference station sites providing corrections over the Loran data channel.
When eLoran is used for positioning, a minimum of three eLoran transmitting sites are needed to calculate a two-dimensional position fix and time. Time (phase) and frequency can be derived from a single transmitting site as well. With three sites, timing can be derived while a receiver is in motion. An integrated eLoran/GPS receiver can take advantage of combinations of eLoran and GPS transmissions to develop a PNT solution. Any additional measurements provide a means to improve the solution’s accuracy (using weighted least squares) or to protect the solution’s integrity (by receiver-autonomous integrity monitoring).
To achieve the highest accuracy levels, the user receiver corrects its TOA measurements with the published ASF values for the area and differential eLoran corrections received through the Loran data channel. ASF maps for specific geographic areas are distributed to users in a receiver-independent data format that is currently being standardized by the Radio Technical Committee for Maritime Services’ (RTCM’s) Special Committee (SC) 127 on eLoran. The ASF map data would be published by the service provider responsible for aids to navigation.
As described before, the measured ASF values remain stable over long periods of time. Any small changes in the published ASFs due to changes in propagation path characteristics or transmitter-related delays will be compensated for by differential corrections. For this, a differential eLoran reference station site is deployed within 20 to 30 miles (32 to 48 kilometers) of the area of interest. The reference station compares its measured ASFs against the published values and broadcasts corrections to the users through the Loran data channel. Figure 1 shows the principle of differential eLoran positioning in a maritime environment and is representative of its use in other modalities as well.
Figure 1. Overview of a representative eLoran system.
eLoran meets the application requirements shown in Table 1. While unaided, Loran-C does not meet the requirements for a multi-modal, redundant PNT system, specifically the position accuracy requirement. The U.S. first developed eLoran to reduce the positioning error and to enable the system to meet modal performance requirements.
Table 1. eLoran system performance requirements.
eLoran Applications
We are staunch advocates of GPS and believe it should be fully funded, kept technically advanced, protected, toughened and augmented. When GPS is available and trustworthy, it should be used. However, no technology is failsafe, and prudent users should not rely on a sole source for their PNT needs. GPS has been called “a single point of failure” for much of the U.S. economy and critical infrastructure. Applications and requirements vary widely from wireless network communications of ± 1.5 microseconds, to maritime harbor entrance and approach requirements of ± 20 meters, to phasor measurement unit requirements in the electric power grid of ± 500 nanoseconds.
It is important to recognize the challenge of providing assured PNT while also taking advantage of the efficiencies gained by implementing a common solution across all sectors, industries and users. Point solutions can provide complementary PNT for specific individual or modal needs, and any resilient PNT ecosystem includes multiple levels of redundancy.
Some key application areas in which eLoran can provide complementary PNT are telecommunications, energy, finance and transportation. We believe these will be some of the first sectors to adopt and exploit eLoran as a component of their critical infrastructure protection and possibly as a co-primary PNT solution alongside GPS.
Telecommunications Sector. A March 2014 letter from the Alliance for Telecommunications Industry Solutions (ATIS) to the National Security Telecommunications Advisory Committee contained an attached document, Recommended Updates to Telecom Vulnerability to Loss of GPS Signals Documentation, that outlined three areas of concern that ATIS has identified relating to the exposure of commercial communications systems to a loss of the GPS signal. Included in the documentation was the statement: “With the Loran systems decommissioned, GPS is currently the only technology that can meet synchronization requirements for E911 as there is no other widely available access to UTC time of day in the United States.” eLoran’s Loran data channel provides the UTC time-of-day information that the telecommunications industry seeks, as well as providing complementary timing (phase) and/or frequency solutions that would mitigate ATIS’s concerns about: (1) the size of the area and duration effects of a GPS outage, (2) the effects of spoofing, (3) the inability of oven-controlled crystal oscillators (OCXOs) to maintain phase alignment for 24 hours at 1.5 microseconds, and (4) the phase performance of OCXOs in varying temperature environments.
The European Telecommunications Standards Institute Primary Reference Clock mask is one tool used by the telecommunications industry to determine the quality of timing signals in telecommunication applications. Figure 2 shows that eLoran is able to meet maximum time interval error (a measurement of wander or time stability) requirements, often outperforming GPS. Testing was performed independently in a cooperative effort between the United Kingdom National Physical Laboratory and Chronos Technology Ltd., UrsaNav’s reseller in England.
Figure 2. Maximum time interval error plot of eLoran and GPS.
Energy Sector. At present, GPS is the only time source for phasor measurement unit (PMU) (also known as synchrophasor) and frequency data recorder (FDR) sensors used to collect data that measures the state of an electrical system and manages power quality. PMUs/FDRs are a necessary component of the movement to a smart-grid approach to improve energy efficiency on the electrical grid and in businesses and homes. PMUs and FDRs cease to work if the GPS signal is lost or unstable. In 2013, UrsaNav began working with the University of Tennessee at Knoxville (UTK) to demonstrate the capability of eLoran, alongside GPS, to provide the necessary timing accuracy for UTK’s high-precision FDRs to collect synchrophasor data from the U.S. power grid. The required accuracy of the timing reference source is ± 500 nanoseconds, needed by each device performing synchrophasor measurements.
The laboratory setup in Bedford, Mass., used side-by-side FDRs: one using a GPS receiver and one using an eLoran receiver. Other than replacing the GPS receiver with an eLoran receiver in one of the FDRs, no other changes were made. The eLoran signals were being transmitted from a former U.S. Coast Guard (USCG) Loran Support Unit in Wildwood, N.J., more than 300 miles (483 kilometers) from our Bedford laboratory.
“Raw” eLoran was used for the test, that is, with no differential corrections nor continuous receiver antenna calibration. Figure 3 shows the resultant frequency and phase angle comparisons between GPS and eLoran. Green is eLoran; black is GPS. Frequency comparisons are on the left, top and bottom. Phase angle comparisons are on the right, top and bottom. The bottom left graph is a blow-up of the area encircled in red in the top left graph. The bottom right graph is a blow-up of the area encircled in red in the top right graph. In both cases, eLoran performs on par with GPS.
Figure 3. Frequency data recorder outputs from GPS and eLoran.
Financial Sector. A European Securities and Markets Authority (ESMA) report, dated May 22, 2014, indicates that the majority of trading venues are already coordinated with GPS time, and further states that the deployment of these systems might be costly and technically challenging. ESMA’s view is that each trading venue and market participant should rely on an atomic clock to issue timestamps. An eLoran timing alternative would be less costly, less technically challenging, and, when used in concert with other solutions (such as GPS, atomic clocks or Network Time Protocol / Precision Time Protocol) would also provide trusted time. eLoran would provide absolute time over very wide areas, thereby allowing dispersed markets and users to take advantage of this synchronized time solution. Additionally, eLoran can often provide time indoors, using a magnetic field (H-field) antenna, thereby precluding the permits and expense required for a rooftop antenna installation. ESMA has asked for industry comment on its proposed requirement to synchronize clocks to the microsecond level, and invited industry responses to its preliminary view that business clocks be accurate at least up to the microsecond level.
Transportation Sector – Aviation. PNT use in air traffic management is illustrative. In accord with U.S. Federal Aviation Administration (FAA) planning, a principal surveillance source in the U.S. national air space (NAS) by 2020 will be Automatic Dependent Surveillance-Broadcast (ADS-B), where the required positional accuracy of aircraft relies on GPS position. Moreover, the independent validation and backup of GPS-derived positions relies on accurate time-of-arrival measurements at a network of 650 radio stations in the NAS that currently use GPS-disciplined clocks with accuracy down to 30 nanoseconds. These radio stations are critical infrastructure of the Surveillance and Broadcast Services (SBS) system, which provides ADS-B surveillance to FAA air traffic management (ATM).
The FAA recognizes the need for a backup to surveillance and navigation in the event of local, regional and wide-scale GPS outages, and is examining both near-term and long-term strategies for continuity of operations during those outages. Because of the long lead times for ATM technology insertion, near-term mitigation strategies out to at least 10 years are constrained by existing ATM ground infrastructure and current avionics capabilities. Long-term solutions are not so constrained, and may be based on new signals in space, new ground infrastructure and new avionics capabilities.
Surveillance. Beginning in 2020, ADS-B will be a principal surveillance technology. In recognition of the need for a backup if GPS fails, the FAA is planning to maintain a mix of beacon-interrogation radar and wide-area multilateration (WAM) in the near term. The long-term strategy is still very much in the evolutionary stage.
Navigation. Near-term strategies involve a mix of approaches based upon existing infrastructure and the current capability of avionics. A leading approach, referred to as DME/DME/IRU, uses two-way ranging to multiple Distance Measuring Equipment (DME) facilities augmented by the avionics inertial reference unit (IRU). This approach is practical and applicable more to air carrier aircraft than regional jets or general aviation. Other approaches rely to some extent on the use of very high frequency Omni-Directional Range (VOR) facilities. As with surveillance, the long-term strategy is very much evolutionary.
It is instructive to note that near-term solutions rely on existing radar, DME and VOR infrastructure because it is in place and is compatible with existing avionics. In the long-term view, new technologies with less costly infrastructure are likely to be more cost-effective, especially if they provide benefits beyond ATM applications. eLoran is such a technology.
Transportation Sector – Maritime. There is an increasing awareness in the maritime world that no single system can provide PNT resiliently under all circumstances. At this moment, GPS (with augmentations) is used on most commercial vessels, and in many cases integrated into systems we did not expect would need or use GPS-derived position or time. Even though the introduction of GLONASS, Galileo, BeiDou and other GNSS systems will provide some resilience, the underlying (satellite) technology remains the same, only providing relatively weak signals from space at mostly the same or close-by frequencies for compatibility and inter-operability.
The International Maritime Organization (IMO) recognizes the need for multiple PNT systems on board maritime vessels. The organization developed the e-Navigation concept to increase maritime safety and security via means of electronic navigation, which calls for at least two independent dissimilar sources of positioning and time in a navigation system to make it robust and fail safe. As a follow on, IMO’s Navigation, Communications and Search and Rescue Committee is considering performance standards for multi-system shipborne navigation receivers, which includes placeholders for satellite, augmentation and terrestrial systems.
The most viable terrestrial system providing PNT services that meet IMO’s requirements is eLoran. With three eLoran transmitters in good geometry, eLoran can provide sub-10 meter (95 percent probability level) horizontal positioning accuracy and UTC synchronization within 50 nanoseconds, sufficient to be the co-primary PNT solution with GNSS. The General Lighthouse Authorities of the United Kingdom and Ireland (GLAs) have installed UrsaNav’s differential eLoran reference stations to provide the world’s first initial operational capability (IOC) eLoran system.
Together with Loran transmitters in England, France, Germany, Norway and Denmark, the differential eLoran reference stations provide better than 10-meter positioning accuracy at seven ports and port approaches along the English and Scottish east coast. IOC was achieved at the end of 2014, with full operational capability planned for 2018. Other nations have either begun, or are exploring, similar projects.
Figure 4 shows the accuracy of an eLoran position at the differential reference station on the Humber River in England. Figure 5 shows the position accuracy while on board a vessel transiting outbound on the river from Humber to the North Sea.
Figure 4. Zero-baseline accuracy at Humber reference station.Figure 5. Onboard, en route accuracy on the Humber River.
Current State of eLoran Technology
eLoran technology has been available since the mid-1990s and is still available today. In fact, the state-of-the-art of eLoran continues to advance along with other 21st-century technology. eLoran system technology can be broken down into a few simple components: transmitting site, control and monitor site, differential reference station site and user equipment.
Modern transmitting site equipment consists of a high-power, modular, fully redundant, hot-swappable and software configurable transmitter, and sophisticated timing and control equipment. Standard transmitter configurations are available in power ranges from 125 kilowatts to 1.5 megawatts. The timing and control equipment includes a variety of external timing inputs to a remote time scale, and a local time scale consisting of three ensembled cesium-based primary reference standards. The local time scale is not directly coupled to the remote time scale. Having a robust local time scale while still monitoring many types of external time sources provides a unique ability to provide proof-of-position and proof-of-time. Modern eLoran transmitting site equipment is smaller, lighter, requires less input power, and generates significantly less waste heat than previously used Loran-C equipment.
The core technology at a differential eLoran reference station site consists of three differential eLoran reference station or integrity monitors (RSIMs) configurable as reference station (RS) or integrity monitor (IM) or hot standby (RS or IM). The site includes electric field (E-field) antennas for each of the three RSIMs.
Modern eLoran receivers are really software-defined radios, and are backward compatible with Loran-C and forward compatible, through firmware or software changes. ASF tables are included in the receivers, and can be updated via the Loran data channel. eLoran receivers can be standalone or integrated with GNSS, inertial navigation systems, chip-scale atomic clocks, barometric altimeters, sensors for signals-of-opportunity, and so on. Basically, any technology that can be integrated with GPS can also be integrated with eLoran.
Figure 6 shows a resilient PNT receiver that includes GPS, DGPS, eLoran and a dual-band (100/300 kHz) E-field antenna. The left-hand antenna, shown installed on the P&O Ferries’ Pride of Hull, is the resilient PNT antenna. The right-hand antenna is a standard GPS antenna.
Figure 6. Resilient PNT receiver and dual-band antenna.
World View of eLoran
Nine nations are operating Loran-C or eLoran stations, including Russia and China. It is our understanding that the Republic of Korea, India and the Kingdom of Saudi Arabia are pursuing the installation of eLoran technology or upgrading their Loran-C technology to eLoran.
The modernization and upgrade of the U.S. Loran-C system to eLoran was a congressionally mandated program jointly executed by the FAA and USCG from 1997 to 2009, and funded at $160 million. During this time, eLoran was successfully tested and demonstrated in all modes: aviation, maritime, land-mobile, location-based, and timing and frequency. Further, eLoran has been successfully in operation in the U.K. for several years. Every national and international government, industry and academic report has concluded that GNSS is vulnerable and that eLoran is the best complementary solution to help negate those vulnerabilities.
The U.S. terminated its Loran-C service, and thereby its nascent eLoran program, in 2010. Canada followed suit and terminated its Loran-C service as well. Shortly thereafter, DHS/USCG began dismantling or demolishing the modernized infrastructure. However, in December 2014, Congress directed that DHS/USCG preserve the existing, unused U.S. Loran-C infrastructure, unless the Secretary of Homeland Security certifies it is not needed for a system to complement GPS.
In March 2015, U.S. House of Representatives Resolution (H.R.) 1678, a bill that would require establishment of a strong, difficult-to-disrupt terrestrial system to complement GPS, and to serve as another source of PNT when GPS isn’t available, was referred to the Committee on Armed Services. The bill seeks to amend the language that provided for the establishment and management of GPS in Title 10, the section of law that deals with the armed services. We understand that other members of Congress have expressed interest and will be co-sponsoring the bipartisan bill. H.R. 1678 was introduced by Congressman John Garamendi (Democrat, Calif.) with Congressman Duncan Hunter (Republican, Calif.), Congressman Frank LoBiondo (Republican, N.J.) and Congressman Peter DeFazio (Democrat, Ore.) as the initial co-sponsors. In August, the bill was referred to the Subcommittee on Strategic Forces.
Additionally, in May 2015, the DHS and USCG entered into a cooperative research and development agreement with UrsaNav and Exelis (now part of Harris Corp.) to research, evaluate and document at least one alternative to GPS as a means of providing PNT information in the form of eLoran.
It is our understanding that the U.S. Congress is still considerably concerned about the lack of a complementary PNT solution to safeguard U.S. critical infrastructure and key resource sectors, and to protect our economy in the event of a GPS outage. Congress continues to press the administration for a resolution, in the form of a continental U.S. eLoran system, before our nation is placed at further risk.
Acknowledgments
The authors wish to acknowledge the assistance of Dr. Ron Bruno, Harris Corp., and Dr. Paul Williams and Chris Hargreaves, GLAs.
Manufacturers
UrsaNav provided the eLoran receiver and Symmetricom, now Microsemi, provided the GPS receiver for the timing tests shown in Figure 2.
STEVE BARTLETT is vice president of operations at UrsaNav, Inc., North Billerica, Mass.
GERARD OFFERMANS is senior research scientist at UrsaNav engaged in various R&D project work and product development.
CHARLES SCHUE is co-owner and president of UrsaNav.
FURTHER READING
eLoran
“Can eLoran Deliver Resilient PNT?” by N. Ward, C. Hargreaves, P. Williams and M. Bransby in Proceedings of The Institute of Navigation 2015 Pacific PNT Meeting, Honolulu, Hawaii, April 20–23, 2015, pp. 1051–1054.
“eLoran Initial Operational Capability in the United Kingdom – First Results” by G. Offermans, E. Johannessen, S. Bartlett, C. Schue, A. Grebnev, M. Bransby, P. Williams and C. Hargreaves in Proceedings of the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif., January 26–28, 2015, pp. 27–39.
“Implementing a Wide Area High Accuracy UTC Service via eLoran” by G. Offermans, E. Johannessen and C. Schue in Proceedings of the 46th Annual Precise Time and Time Interval Systems and Applications Meeting, Boston, Mass., December 2014, pp. 124–133.
2014 Federal Radionavigation Plan (F, DOT-VNTSC-OST-R-15-01, U.S. Department of Defense, Department of Homeland Security and Department of Transportation, Washington, D.C., available from the National Technical Information Service, Springfield, Virginia, 2015.
1990 Federal Radionavigation Plan, DOT-VNTSC-RSPA-90-3 and DOD-4650.4, U.S. Department of Transportation and U.S. Department of Defense, Washington, D.C., available from the National Technical Information Service, Springfield, Virginia, 1990.
