Not “fly by,” but “apply.” As in W. Somerset Maugham’s advice to aspiring young writers: Apply the seat of the pants to the seat of the chair.
Get grounded. Confront the blank page, the typewriter, or the less preferable modern equipments (because instant electronics short-circuit orderly brain function) for a period of silence and contemplation. Above all, think. Then, and only then, communicate.
Some serious word-eating now ensues. In last month’s editorial, I faulted the Architecture Evolution Plan (AEP) GPS ground control software update 5.5C for wreaking havoc with fielded receivers, both military and civil. Website news stories that I subsequently posted bore headlines driving this misconception home.
I was wrong. Subsequent analysis by more knowledgeable and expert people has established that, yes, havoc was wreaked, but not by the software update. The deficiency resided in the factory-installed software of the respective receivers. See The System story on page 12 for details.
In my only defense, I state that I was groping in the dark. Very incomplete word, secondhand no less, arrived just as we were preparing the issue for press. No official announcement came from the Air Force during the days that followed. We understand that internal brouhaha was brewed, stern words were uttered and written, but ultimately it was determined that the fault lay in the user segment, not with ground control or vendors thereto.
We have it on good authority that the GPS Wing maintains, and will presumably continue to maintain, that the AEP update was absolutely perfect with no software glitches. But the Wing also realizes that none of the receiver manufacturers had any idea how the update was going to be implemented, as in what pages would be affected, and if there would be new data in places where it had not been loaded before.
A grudging admission emerges that there may be some ambiguity in the receiver interface control documents (ICDs), and that those may need some tightening up sometime soon.
We return to the theme of communications, in this case clear ones between the GPS Wing and the various user communities: civil, commercial, and military. For GPS to maintain its place as the world’s gold standard, there must be clear — and timely — communication between the Wing and its customers. All customers. Dual-use.
So far, there’s no No. 2 system trying harder in this regard. But why leave the door open?
A controversial suggestion: beta versions of future AEP updates could be released to a predefined subset of receiver maufacturers, who would test and report back concerning any glitches that occurred when their receivers saw the new software and simulated nav message for the first time.
After reviewing current performance of WAAS, EGNOS, and MSAS, the authors present expected future performance, including the benefits of GPS L5. They evaluate the impact of the Indian GAGAN and Russian SDCM systems on global coverage and examine southward expansions for the original three SBASs. Finally, a look at the impact of a second constellation of navigation satellites and the performance for a user taking advantage of two core constellations.
By Todd Walter, Juan Blanch, and Per Enge, Stanford University
The Wide Area Augmentation System (WAAS) monitors GPS and provides both differential corrections to improve accuracy and associated confidence bounds to assure integrity. The first satellite-based augmentation system (SBAS), it was commissioned for service in 2003. Japan’s MTSAT-based Satellite Augmentation System (MSAS) was commissioned in 2007, and the European Geostationary Navigation Overlay Service (EGNOS) was declared operational in 2009, with safety-of-life service commissioning expected in mid-2010. Two other SBASs are in the developmental stage: India’s GPS Aided Geo Augmented Navigation (GAGAN) and Russia’s System for Differential Corrections and Monitoring (SDCM) have fielded equipment and plan to become operational in the next few years.
Coming improvements will expand SBAS coverage areas and strengthen their performance. In the near term, these include more monitoring stations and algorithmic enhancements, with incorporation of a second civil signal in a protected aeronautical band and new GNSS constellations in the long term.
An SBAS utilizes a network of precisely surveyed reference receivers, located throughout its coverage region. The information gathered from these reference stations monitors the GNSS satellites and their propagation environment in real time. Availability of SBAS service is a function of two quantities: the arrangement of the pseudorange measurements used to determine the user’s position, referred to as geometry; and the quality of each individual measurement, referred to as the confidence bound. Although very small confidence bounds can make up for poor geometries, and strong geometries can overcome large confidence bounds, both values are generally required to be good to obtain high availability.
Geometry is determined purely by the locations of the ranging satellites relative to the user. Currently the basic geometry is provided by the GPS constellation. Historically it has exceeded commitments, and there are currently 29 healthy satellites in orbit when only 21 are nominally guaranteed. However, as satellites are taken off-line in critical orbital slots, the quality of the geometry can degrade significantly. There could be short duration losses of service daily at some locations. Since the goal is to provide service more than 99.9 percent of the time, these outages can have a dramatic impact. WAAS currently mitigates this concern by adding geostationary satellites with a ranging function virtually identical to the GPS satellites. These satellites are always in view and improve the overall geometry, although they do not eliminate the problem completely.
The confidence bounds relate to the expected error sources on the range measurements. Currently three error sources are corrected via broadcast to the user: satellite clock error, satellite ephemeris error, and delay error due to propagation through the ionosphere. These error sources are described by two confidence bound terms: the user differential range error (UDRE) for the satellite errors, and the grid ionospheric vertical error (GIVE) for the ionospheric errors.
For single-frequency SBAS, this last error source is the most significant. Users may sample the ionosphere anywhere in the service volume, but the SBAS only has measurements from its reference station locations. Thus, there is always the possibility of undetected ionospheric disturbances. This leads to larger confidence bounding terms and lower availability.
The combination of geometry and confidence bounds yields the protection levels (PL). PLs are the real-time confidence bound on the user’s position error. To match aviation requirements these are broken into a vertical protection level (VPL) and a horizontal protection level (HPL). Each SBAS guarantees that the user’s actual position error will be smaller than these values 99.99999 percent of the time. The PLs are calculated in real-time using stored and broadcast information. They must be compared to the maximum allowed value for a desired operation. The upper bounds are called alert limits (AL) and they are fixed numbers whose values depend on the operation.
In this article we are interested in the localizer performance with vertical guidance (LPV)-200 approach with a VAL of 35 meters and HAL of 40 meters. Currently, LPV aviation approaches can only be accomplished with a WAAS GPS receiver. Performance of an LPV approach allows minimums as low as 200 feet above ground level before a missed approach must be executed. As of January 2010, there were 1,930 published WAAS LPVs, with plans to add 300 per year in the United States.
Because GPS and SBAS generally perform better at horizontal positioning than vertical, the requirement that the VPL be below the VAL is nearly always the limiting constraint for these operations.
Methodology
To determine the global availability and the effect of potential improvements, we used our Matlab Algorithm Availability Simulation Tool (MAAST). This tool uses almanac data to calculate the position of the satellites for each specified epoch. The almanac chosen for this study corresponds to the GPS almanac broadcast on April 8, 2009, when there were 30 healthy satellites. However, PRNs 25 and 32 were removed to simulate a condition with 28 healthy satellites. MAAST also implements the WAAS integrity algorithms to calculate the corresponding UDRE and GIVE values. Finally, it uses these values to implement the airborne algorithms specified in the minimum operational performance standards (MOPS) for SBAS. The MOPS specifies user algorithms for determining the protection levels. For these simulations, the VPL and HPL are calculated about every 5 minutes and every two and a half degrees across the globe.
MAAST does a good job of predicting WAAS behavior. It is less accurate when predicting other systems’ performance. EGNOS has developed its own monitoring receivers and integrity algorithms and has different criteria for assigning a satellite a particular UDRE value and assigning each ionospheric grid point’s (IGP’s) GIVE value. Nevertheless, both systems are designed to meet ICAO requirements for integrity, and their performance should be somewhat similar. In observing EGNOS coverage plots and comparing them to MAAST predictions, we do see differences. However, the size of the coverage region and approximate boundaries are reasonably close and provide an idea of performance if not an exact map.
The MSAS algorithms are based upon the same algorithms used in earlier versions of WAAS. Therefore, MAAST should be slightly more accurate in modeling its performance. GAGAN uses the same prime contractor as WAAS and therefore similar algorithms may be expected. Less is known about the intended SDCM algorithms and therefore the modeling of this system faces the largest uncertainty. Again, the MAAST predictions should be viewed as indicative rather than precise. Individual availability maps will not be completely correct, but relative performance improvements should be properly indicated.
Current Systems Status
Currently WAAS is in its full LPV-200 performance (FLP) phase. It consists of 20 WAAS reference stations (WRS) in the conterminous United States (CONUS), in addition to seven in Alaska, one in Hawaii, one in Puerto Rico, four in Canada, and five in Mexico for a total of 38. The station locations are shown as blue circles in Figure 1. There are three WAAS master stations (WMS) and two geostationary satellites (GEOs). The GEOs are the Intelsat Galaxy XV satellite
at 1338 W and the Telesat ANIK F1R satellite at 1078 W.
FIGURE 1. Existing SBAS reference networks, consisting of 38 reference stations for WAAS, 34 for EGNOS, and 8 for MSAS.FIGURE 2. Simulation results from MAAST for availability of LPV-200 provided by current systems.
As can be seen in Figure 2, availability of LPV-200 service is very high for most of North America. In general, this performance meets the goals for the system. However, in some regions performance is lower than the 99 percent minimum target. The West Coast, Alaska, and Southern Mexico all suffer from reduced availability.
MSAS is in its initial operating phase. It consists of six ground monitoring stations (GMSs) on the Japanese Islands, one in Australia, and one in Hawaii (magenta triangles in Figure 1). There are two master control stations (MCSs) and two Multifunction Transport Satellite (MTSAT) geostationary satellites at 1408 E and 1458 E.
Because of the limited network size, the GEO UDREs for MSAS are set to 50 meters and therefore do not benefit vertical guidance. Further, the limited ionospheric observations offer little availability of LPV-200 service as can be seen in Figure 2. As a result, vertically guided operations have not yet been authorized based upon MSAS. The Japanese Civil Aviation Bureau (JCAB) has studied performance improvements that could allow it to provide LPV-200 operations. Until then, MSAS provides only lateral navigation.
EGNOS is also in its initial operations phase. It consists of 28 ranging and integrity monitoring stations (RIMS) in Europe, one in Turkey, three in Africa, one in North America, and one in South America (green squares in Figure 1). There are four master control centers (MCCs) and two GEOs, the INMARSAT Atlantic Ocean Region-East (AOR-E) satellite at 15.58 W and the ARTEMIS satellite at 21.58 E.
For a variety of reasons, EGNOS has chosen to implement its GEO satellites without a ranging capability. Thus, for our simulations we have set them as data-links only and do not model a ranging capability. EGNOS also currently implements Message Type 27 (MT-27) rather than Message Type 28 (MT-28) as do WAAS and MSAS. MT-27 restricts the use of low UDRE values to a box centered on the European region. Its borders can be discerned in Figure 2. Currently it has little impact on LPV-200 service, but if EGNOS is to expand its coverage, it may become a limiting factor. Availability of LPV-200 service is very high for most of Europe. However, there is a desire to expand coverage to more reliably cover Iceland, Scandinavia, Eastern Europe, and the Mediterranean and South Atlantic regions.
Near-Term Improvements
EGNOS is fielding additional reference stations in the Canary Islands, Northern Africa, and the Middle East. In the longer term, MT-28 is being considered as a replacement for MT-27. In our modeling we added seven new RIMS, shown in Figure 3, and implemented MT-28. We also improved the ionospheric mask by including additional IGPs. We did not update GEO locations nor did we model ranging capability that could further enhance performance. By comparing
FIGURE 3. Improved SBAS networks. The newly added reference stations are marked by yellow filled squares for EGNOS and yellow filled triangles for MSAS.
Figure 4 to Figure 2 improvements can be seen, in particular expanded LPV-200 operation to the south.
FIGURE 4. Improved single frequency SBAS coverage for the original three SBAS.
The future of MSAS improvements is less certain, with no firm commitments for major service enhancements. We have chosen to model fairly aggressive enhancements based upon studies made by the Electronic Navigation Research Institute in Japan. We have added 10 new reference stations in Japan and made the ionospheric threat model less conservative, in line with current WAAS algorithms. Together, these improvements offer good vertical guidance coverage over Japan.
These improvements extend coverage in the vicinity of the reference station networks, but are unable to push availability much beyond. This is primarily due to the limitations of the ionospheric corrections. Because strong gradients can exist outside of the viewing area of the network, tight confidences cannot be provided to those regions.
SBASs model the ionosphere as a thin 2-dimensional shell 350 kilometers above Earth. This works well for quiet mid-latitude and polar ionosphere. However, equatorial ionosphere often has significant vertical structure that is not well replicated by the SBAS message. The resulting confidence bounds are then too large to reliably provide LPV-200 capability. No certified algorithm capable of bounding the equatorial ionosphere is known to the authors. Instead, it is recommended that SBASs in equatorial areas wait for the forthcoming L5 signal to provide vertical guidance in their regions.
GPS L5
The next GPS satellite to be launched will contain a new civil signal, L5, centered at 1176.45 MHz and in a protected aviation band. As such, it will be approved for use on aircraft. When the L5 signal is used in combination with L1, the ionospheric delay for each line-of-sight can be directly estimated. This will dramatically lower the uncertainty of the pseudorange measurement. Thus, if the SBAS is upgraded to provide corrections appropriate for an L1/L5 user and the user similarly upgrades his or her avionics, SBAS service can be dramatically improved.
Another important advantage of the second civil frequency is its relative immunity to ionospheric storms. Because the users are now directly eliminating the amount of delay they actually experience, they are no longer affected by shortcomings in the MOPS ionospheric model. The weaker effect of scintillation may have some impact; however, we do not expect to lose vertical guidance altogether. Furthermore, the availability of two civil frequencies offers protection against unintentional interference. If either L1 or L5 is jammed, the user still has access to guidance on the available frequency.
At the moment there is no MOPS for an L1/L5 user, so any ground or user algorithms will have to be speculative. We propose basing future L1/L5 algorithms on the existing L1-only algorithms. Instead of using L1-only pseudorange measurements, the user forms the ionosphere-free combination. For the confidence term representing the total pseudorange error on a line-of-sight, the ionospheric correction terms and airborne multipath terms are replaced with a single value representing airborne noise and multipath for the ionosphere-free combination.
For a single frequency user, each line-of-sight has four confidence terms that are summed together to obtain the total confidence. These terms correspond to: the satellite clock and ephemeris corrections (σflt), the ionospheric correction (σUIRE), the airborne code noise and multipath (σair), and the troposphere (σtrop). The total one-sigma confidence bound for a particular line-of-sight is the root sum square (RSS) of these four terms:
(1)
When a user has access to two civil frequencies, they can remove the ionospheric effects by forming the iono-free combination of the two pseudoranges:
(2)
where f1 and f5 are the L1 and L5 frequencies (1575.42 MHz and 1176.45 MHz) respectively. If σ1 and σ5 are comparable then the iono-free combination has roughly three times as much noise as either single frequency term, but is substantially smaller than σUIRE . Furthermore, satellites do not need a grid correction to be used, thus satellites farther from the network and IGP mask can be incorporated into the position solution. The dual-frequency confidence bound for a single satellite is then given by
(3)
where σair is used in place of σ1 and σ5 in (2).
For the VPL we propose adding nominal bias terms to handle observed signal biases and non-Gaussian behavior of the underlying error terms. By including these terms it is possible to reduce the net impact of these biases on the user. Further, we propose tailoring the VPL equation to the most significant remaining threat to the user: single satellite fault modes. The L1-only VPL equation is appropriate for threats that affect many signals simultaneously as may happen with the ionosphere or troposphere. However, with the user directly eliminating ionospheric effects, the most significant threats come from satellite fault modes. As these faults are rare, they are unlikely to affect more than one ranging measurement at a time. Therefore, a VPL can be constructed to explicitly account for such a threat. We recommend that the dual frequency VPL take the following form:
(4)
where KHMI and σ5 is the Gaussian tail factor corresponding to the probability of Hazardously Misleading Information, s3,i is the projection of the pseudorange error onto the vertical position estimate, sff is the fault free overbounding sigma, biasnomis the nominal bias bound, Kfault is the Gaussian tail factor accounting for the probability of fault, and biasfault is a bound on the magnitude of all satellite faults. The H0 condition corresponds to the most likely condition of no faults present. The H1 condition corresponds to the unlikely event of a fault on the dominant satellite. The final VPL is the maximum across both conditions.
Because the faulted bias term covers the satellite faults the fault-free sigma term, σff, can be much smaller than the current total value (1), or the dual frequency version (3). Further, since the probability of fault is small, Kfault can be much smaller than KHMI . The net result is that the proposed VPL is smaller than the existing VPL for the same conditions. To model L1/L5 availability we chose the following parameters:
KHMI = 5.33
Kfault = 2.33
σ 2ff = (σflt / 3 ) 2 + σ 2iono_free + σ 2trop
biasnom = 0.5 m
biasfault = 5.333 x σflt
Other values follow the single frequency MOPS specifications as normally implemented by MAAST.
Given these parameters, the H1 hypothesis nearly always dominates the VPL calculation. We have used a nominal weighting scheme to optimize for accuracy. It is possible to deweight the dominant satellite to improve availability. We will be looking at practical methods for determining more optimal weighting for the VPL given in (4). However, there is a concern that such optimizations could harm accuracy. The potential benefits vs. risks will be studied.
The improvement in performance for a dual-frequency user can be seen in Figure 5. The coverage is significantly expanded. Now each region is robustly covered with large margins surrounding their intended service regions. However, coverage is still limited to the areas around these first three SBASs.
FIGURE 5. Potential dual frequency coverage of the first three SBASs including network improvements.
GAGAN and SDCM
Two additional SBASs are currently under development that will extend coverage to more regions. India is developing GAGAN. Currently it has eight Indian reference stations (INRES) all in India (blue diamonds in Figure 6). There is one Indian master control center (INMCC), and plans to use the GSAT-4 as its initial GEO. The GSAT-4 is planned for launch in 2010 and will be located near 82° E. The geomagnetic equator passes through India and it therefore faces the full impact of equatorial ionosphere. The advent of L5 will allow GAGAN to obtain high LPV-200 availability that is unlikely to be achievable for single-frequency users.
FIGURE 6. The networks of five SBAS systems are shown. In addition to the reference stations from Figure 3, the 8 Indian stations are shown as blue diamonds and the 19 Russian stations are shown as red stars.
Russia is developing SDCM. It now has nine operational measuring points (MPs) and has plans for at least 10 more locations, all in Russia (red stars in Figure 6). There are also plans to use three GEOs: Luch-5a planned for launch in 2010 and to be located near 16° W, Luch-5b planned for launch in 2011 and to be located near 95° E, and Luch-4 planned for launch in 2013 and to be located near 167° E.
Figure 7 shows the combined dual-frequency coverage of all five systems, WAAS, EGNOS, MSAS, GAGAN, and SDCM.
FIGURE 7. The combined dual frequency availability of the five SBASs is shown.
The vast majority of land masses in the northern hemisphere are now well covered by at least one of the SBASs. Figures 6 and 7 clearly highlight that the majority of development has occurred in the northern hemisphere. In fact, only two reference stations have been placed below the Equator.
Southern Hemisphere
If SBAS is to provide a global solution, its coverage must extend into the southern hemisphere. There have been many discussions with representatives of countries in the southern hemisphere. Further, the United States has had testbed receivers in South America for nearly 15 years. Europe has fielded receivers in Africa. Australia investigated its own variant of SBAS called the Ground-based Regional Augmentation System (GRAS). However, we are not aware of concrete plans for development in this hemisphere.
We anticipate that discussions will eventually evolve into firm plans and that either independent SBASs will be developed in these regions or existing SBASs will expand their networks southward. We have chosen to assume that WAAS, EGNOS, and MSAS will expand their networks to extend LPV-200 coverage to the southern portion of their GEO footprints. This is but one of many possible scenarios. The pr
oposed expansion shown in Figure 8 is not based on any plans, but is based on the notion that civil aviation authorities will want to obtain global coverage. The assumed new southern reference stations are shown as yellow-filled circles for WAAS in South America, yellow-filled squares for EGNOS in southern Africa, and yellow-filled triangles for MSAS in and around Australia. Advantages of dual frequency allow us to have much less dense networks for the expansions, in addition to allowing LPV-200 capability to be obtained in equatorial areas.
FIGURE 8. The networks of the five SBAS systems including hypothetical expansions into the southern hemisphere
Figure 9 shows the combined dual-frequency coverage for these SBASs with the expanded network. Now nearly all land masses have good LPV-200 coverage. Note that we have not attempted to optimize these networks to assure coverage to all land masses, not have we tried to find the minimum number of stations that offer this capability.
FIGURE 9. The combined dual frequency availability of the SBASs with the southern hemisphere stations is shown.
Added Core Constellations
Galileo is envisioned as compatible with GPS in that each satellite provides ranging using signals covering the L1 and L5 frequencies with similar modulations. Although the final specifications are not yet set, it is envisioned that Galileo satellites will provide a service that is fully interoperable with the GPS civil signals. Thus, we can approximately model Galileo satellites as being equivalent to GPS satellites in different orbits. In parallel, China is developing the COMPASS system whose signals are also planned to be compatible with GPS.
The Russian GLONASS system has been operational for many years. However, its current signal structure makes it less suited for incorporation into avionics. There are modernization plans to broadcast L1 signals that are more in alignment with the other constellations. Thus it, too, may one day be incorporated into SBAS. We believe that SBASs will someday broadcast satellite clock and ephemeris corrections for GPS and one or more other core constellations. These corrections will remove any difference in the reference times or coordinate frames between the two systems, allowing the corrected signals to be considered fully interchangeable.
Adding 24 or more extra ranging sources will have tremendous benefit for all civil GNSS users. The user’s geometry would be very robust to the loss of one or two satellites. Adding one or more core constellations has the potential to significantly improve SBAS coverage. We chose to model the addition of one constellation, by combining the almanac we used for GPS with one that had been proposed for Galileo. For these scenarios, MAAST is modeling 55 medium earth orbiting navigation satellites in addition to the GEOS used by each SBAS. Because the orbital repeat period is approximately 10 sidereal days for Galileo, the simulated time step and total run time were each increased by a factor of ten.
Figure 10 shows the improved coverage when the reference stations shown in Figure 6 are used. The additional satellites fill in many potential coverage gaps and now, compared to Figure 7, the SBASs all have even more reliable coverage well beyond their reference networks. Indeed, the Northern Hemisphere is now essentially fully covered. Figure 11 shows the results when the expanded networks of Figure 8 are incorporated. Compared to Figure 9, the southern hemisphere is much more reliably covered. The remaining gaps could easily be filled in with just a few more reference stations if full global coverage were desired.
FIGURE 10. The combined dual-frequency, LPV-200 coverage of the five SBAS systems with both GPS and Galileo.FIGURE 11. Combined dual-frequency LPV-200 coverage, SBASs with GPS and Galileo and the southern hemisphere stations.
Conclusions
For single-frequency SBAS the coverage is limited to areas very close to the monitoring station network. However, each region can obtain very good LPV-200 coverage within their desired service area. The addition of GPS L5 makes vertical guidance largely immune to ionospheric disturbances, and permits SBAS coverage to extend into equatorial areas. Independence from the ionospheric grid also allows service to extend farther away from the core network regions. When new Indian and Russian systems are commissioned, a very large fraction of the northern hemisphere will have LPV-200 coverage.
With dual frequency, LPV-200 coverage can be established with comparatively sparse networks in South America, Africa, and around Australia. Additional dual-frequency core constellations such as Galileo, Compass, or GLONASS could greatly expand coverage to well outside the original reference network regions. As GNSS capability is improved and expanded, we anticipate that SBAS coverage may one day provide nearly global LPV-200 or better service capability.
Acknowledgments
The authors acknowledge support of the FAA Satellite Product Office. However, the opinions and potential future scenarios reflect those of the authors and are not necessarily representative of the FAA.
Todd Walter is a senior research engineer at Stanford University. He has been active in the development of the Wide Area Augmentation System and related systems around the globe. His focus is on the provision of certified integrity for aviation applications.
Juan Blanch is a research associate at Stanford University, where he works on integrity algorithms for GNSS. He holds a Ph.D. in aeronautics and astronautics from Stanford.
Per Enge is professor of aeronautics and astronautics at Stanford, where he directs the Stanford GPS Research Laboratory. He has a Ph.D. from the University of Illinois.
Workshop participants from Cote d’Ivoire and Kenya assemble a Mindstorm robot to trial autonomous navigation.
By Patricia Doherty
Last year I helped coordinate a three-week workshop for 50 scientists from 15 African countries, introducing the basics of GPS for applications with socioeconomic benefits and scientific exploration. Held in Trieste, Italy, the workshop was quite successful, producing new initiatives on the African continent. We repeat the workshop next month, April 6–24, again in Trieste.
Since the 2009 training, regional GNSS workshops have taken place in Nigeria, Egypt, Kenya, and Ethiopia. We have initiated scientific collaborations with universities in Nigeria, Kenya, Zambia, Egypt, and Uganda, deploying GPS receivers at each institution, with the understanding that the data will ultimately be shared within Africa and the world.
This effort is a way to share with Africa and Africans the wealth that GNSS has brought to the developed world.
Africa’s 2006 Science and Technology Plan of Action states Africa’s commitment to develop and use science and technology for socio-economic transformation and full integration into the world economy. The leading problems that continue to cripple much of Africa include hunger, extreme poverty, erosion of natural resources, and natural disasters. GNSS can help address these problems and ultimately meet the plan’s goals. Specifically, GNSS applications can increase food security, manage natural resources, provide efficient emergency location services, improve surveying and mapping, and provide greater precision and safety in land, water, and air navigation systems. GNSS also has applications in scientific study including space weather, geophysics, geography, geology, ecology, and biology.
Workshop participants included professors and graduate students from Cote d’Ivoire, Egypt, Ethiopia, Ghana, Kenya, Morocco, Nigeria, Uganda, and Zambia. The more than 25 lecturers came from the United States, Europe, and Africa.
The workshop integrated formal lectures with hands-on practice in GNSS architecture, signal structure, hardware design, state-of-the-art applications, and scientific exploration. An on-site computer laboratory enabled participants to perform positioning calculations; use mapping and surveying software; plan a precision farming procedure; and analyze atmospheric and ionospheric data — all from GPS measurements. In addition, participants built Lego Mindstorm robots to demonstrate autonomous navigation.
One of the benefits of this program was that scientists and engineers from the United States had opportunities to discuss common interests with African scientists and engineers. Many research programs utilize GPS ground- and space-based measurements. Unfortunately, studies over the African region have not been possible due to the lack of dependable long-term measurements. This workshop opened the door to establishing a base of measurements for joint studies with our African colleagues.
Many lecturers remarked that this was the most enriching teaching experience of their careers. The African participants said that they learned a great deal and were very appreciative of the opportunity to participate in this program.
Workshop sponsors include Boston College’s Institute for Scientific Research (where I work), the Abdus Salam International Center for Theoretical Physics in Trieste (where my colleague and workshop co-director Sandro Radicella is head of the Radiopropagation Laboratory), Institute of Navigation, Federal Aviation Administration, Air Force Research Laboratory, National Aeronautics and Space Administration, United Nations Office for Outer Space Affairs, National Science Foundation, Trimble, and NovAtel.
To learn more about the workshop, participate, or contribute, please contact Patricia.Doherty @ bc.edu
Adding GLONASS to GPS gives a total of about 50 satellites, for a significant improvement in navigation availability, reliability, robustness, and convergence time through a new multi-GNSS precise point positioning (PPP) service. System performance and field results demonstrate that there is no need to await future constellations — better performance is available now.
By Tor Melgard, Erik Vigen, Ole Ørpen, Fugro Seastar AS, and Jon Helge Ulstein, Bourbon Offshore Norway AS
Precise point positioning (PPP) stands out as an optimal approach for providing global augmentation services using current and coming GNSS constellations. PPP requires fewer reference stations globally than classic differential approaches, one set of precise orbit and clock data is valid for all users everywhere, and the solution is largely unaffected by individual reference-station failures. There are always many reference stations observing the same satellite because the precise orbits and clocks are calculated from a global network of reference stations. As a result, PPP gives a highly redundant and robust position solution.
The results presented here represent a significant step forward in PPP GNSS research and development. Using GLONASS improves the availability and reliability of the solution. The G2 system’s horizontal positioning accuracy is at the decimeter level. These results derive from increasing the number of satellites in the constellation by 60 percent, from about 30 to 50 satellites. The outcome of the development of the G2 real-time combined GPS and GLONASS PPP service represents a next-generation GNSS augmentation. Further, the later GLONASS-M satellites have improved performance and lifetime over previous GLONASS satellites, so that results will continue to improve as that constellation is replenished.
G2 development has benefited from the close cooperation between Fugro and the European Space Operation Centre (ESOC), an establishment of the European Space Agency (ESA). ESOC has contributed its long experience and expertise on precise orbit and clock processing techniques, while the strength of Fugro is real-time positioning and navigation services.
Based on this work, Fugro has introduced the first real-time GPS and GLONASS precise orbit and clock service. The service utilizes Fugro’s own network of dual-system GNSS reference stations to calculate precise orbits and clocks on a satellite-by-satellite basis for all 50 satellites of the two global navigation satellite systems. The system comprises about 40 dual-frequency GPS and GLONASS reference stations distributed around the world as shown in Figure 1.
Raw GNSS measurement data for all satellites are transmitted to processing centers for calculation of the precise orbit and clock of each GPS and GLONASS satellite (Figure 3). The precise data generated is then broadcast to users via geostationary communications satellites with nearly global coverage, as shown in Figure 2.
FIGURE 1. The G2 reference station network consists currently of 40 GNSS receivers owned and operated by Fugro.FIGURE 2. The G2 precise orbits and clocks are broadcast over redundant geostationary satellite beams together with the other Fugro services.FIGURE 3. Dataflow from the reference stations to the redundant calculation servers producing precise orbits and clocks, then to the satellite uplink stations for broadcast over geostationary satellites to combined G2/GNSS user equipment.
Inside the end-user equipment a dual-frequency carrier-phase-based PPP solution gives horizontal positioning accuracy at the decimeter level. The PPP calculation module is provided by Fugro and is embedded in multiple GNSS receiver manufacturers’ products as well as Fugro’s own product line.
Like any GNSS technique, PPP is affected by satellite line-of-sight obstructions. Even the most precise orbit and clock data is useless if the user cannot track particular satellites. When satellite visibility is partially obstructed, a best possible service can be ensured by using the full range of satellites from both the GPS and GLONASS systems. This can occur during a survey of a dense urban environment, and for urban positioning in general. It can occur under heavy tree cover, when a cruise ship is in a high-sided fjord, when an offshore vessel is close to an oil rig or platform, or during ionospheric disturbances.
The trend clearly lies towards increasing availability of GNSS satellites on orbit; many studies predict the future benefits of combining the constellations of GPS and Galileo. There is no need, however, to wait for future constellations to reap the immediate benefits of access to additional GNSS satellites. The current GLONASS constellation may not have all the features of future GNSS systems, but it is available here and now. Recently, the Russian government has proven its commitment to enhancing the GLONASS constellation. Many receiver manufacturers have also acknowledged this fact and now provide combined GPS and GLONASS receivers.
G2 Accuracy and Statistics
In Figure 4, time-series plots show the 3D accuracy of GPS and GLONASS G2 real-time orbits on August 14, 2009. In the comparison, final orbit data from the International GNSS Service (IGS) is used as reference. PPP positioning is mainly affected by the radial orbit error, which is significantly less than the total 3D error shown here. The 95 percent 3D accuracy for GLONASS (22 centimeters) is more than double that for GPS (10 centimeters). The graph demonstrates how this difference in this case is mainly caused by a few GLONASS satellites being less accurate. Actually, several GLONASS satellites have orbit accuracy very close to the level of GPS for real-time G2 data.
FIGURE 4. GPS and GLONASS orbits compared to IGS final orbits.
Figure 5 shows the clock accuracy of the G2 real-time clocks compared to final IGS clocks. A constant bias has been removed to account for the differences in system reference time. Smaller individual clock biases for each satellite can still be observed. Small biases do not affect the final accuracy of the PPP solution, and achievable position accuracy with these clocks are significantly better than the 21-centimeter 95 percent number for GPS may indicate.
FIGURE 5. GPS clocks compared to IGS final clocks. GLONASS clocks compared to a combined solution based on IGS plus Fugro network to calculate a best possible reference solution.
The lower time series in Figure 5 shows the estimated GLONASS clock accuracy. Currently there is no comparable IGS product with precise GLONASS clocks. A post-processing of all available IGS plus Fugro GNSS stations has been made to establish a reference for the comparison. As shown, the GLONASS clocks are more variable, but still they are stable enough to allow for precise navigation.
Real-Time Positioning Results
Real-time position performance is continuously observed at the G2 operation and monitoring center in Oslo, Norway. The graph in Figure 6 shows typical G2 positioning results with the calculation engine running in dynamic mode at a fixed location for a 24-hour period. The blue lines in the north and east time series are at 20 centimeters and the scale is 61 meter. In the height graph the blue lines indicate the 30-centimeter level. The antenna is in a location with clear view of the sky, and in
dependently calculated reference coordinates are used as reference. 1-sigma accuracy statistics on August 14 are 3, 4, and 8 centimeters in easting, northing and height respectively.
FIGURE 6. G2 GPS-plus-GLONASS position monitoring results in Oslo on August 14, 2009.
Figure 7 shows GLONASS-only real-time positioning with clear view of the sky for the same day as in Figure 6 and the same antenna location. The blue line indicates the 50-centimeter level and the scale is 62 meters. For long periods, the GLONASS-only solution works quite nicely. There are, however, shorter periods with fewer than four satellites being tracked, causing the position output to stop, followed by a period of re-convergence.
FIGURE 7. GLONASS-only real-time PPP solution on August 14, 2009 for a 24-hour period.
Figure 8 displays results from May 11, 2009, when there were slightly more satellites available and just enough to have the GLONASS-only solution running for 24 hours without resets. 1-sigma accuracy statistics for this day are 11, 9, and 16 centimeters in easting, northing, and height respectively. Considering the average number of satellites of 6.14 and periods with high DOP values, this is very promising. In early 2010, 20 GLONASS satellites should be available, and by 2011, 24 are expected. In 2010, a performance similar to or better than that of May 11 should generally be expected with the new satellites. By 2011, even better performance is believed to become the norm of GLONASS-only real-time PPP navigation.
FIGURE 8. GLONASS-only real-time PPP solution on May 11 for a 24-hour period.
Even in some clear-view-of-sky situations, the addition of GLONASS may improve the navigation compared to GPS-only solutions. Figure 9 presents an example of such situations. Here the GPS-only solution suffers some multipath-like effects showing up, especially in the east component. Figure 10 shows the combined GPS+GLONASS solution for the same dataset. The distortion in position is practically eliminated. This is an example where adding GLONASS also improves redundancy and accuracy for navigation with clear view of the sky.
FIGURE 9. GPS-only results for a 3-hour period where some multipath-like effects distort the postition, especially the east component.FIGURE 10. Adding GLONASS improves redundancy and accuracy for the same time period as presented in Figure 9.
The next test further analyzes the same dataset as in Figures 9 and 10 by simulating a virtual wall to the south, blocking all satellites below 40 degrees elevation. Figure 11 illustrates this virtual wall blocking both GPS and GLONASS satellites.
FIGURE 11. GPS and GLONASS satellites blocked between the azimuths 90 and 270 degrees and elevation lower than 40 degrees, effectively establishing virtual wall to the south.
With such data blockage, the GPS-only solution fails for more than 20 minutes, as seen in Figure 12, simply because the number of satellites goes below four. Then a period with slow convergence follows because of few satellites and high DOP.
FIGURE 12. GPS-only solution fails when simulating blockage to the south.
Again, adding GLONASS greatly improves the performance, as shown in Figure 13. Now a sufficient number of satellites are tracked all the time, and there is a continuous solution with the combined GPS+GLONASS throughout the time window when the GPS-only solution failed.
FIGURE 13. GPS+GLONASS solution continues working with simulated blockage to the south.
Even with more than 30 satellites in the GPS constellation, there are situations when the satellite geometry gets poor. This occurred in northwest Europe on February 2, 2010. One of the GPS satellites (PRN17) was not available due to maintenance, and even with five to six usable GPS satellites left, the horizontal dilution of precision (HDOP) was in the range of 7–11 for about 12 minutes (10-degree elevation mask), as shown in figure 14. Such high HDOP values lie above what most user installations are configured to accept, and Fugro received feedback from clients at sea losing positioning. The G2 solution was not affected by the poor GPS geometry and kept the HDOP below 2 during this period, as shown in Figure 15.
FIGURE 14. GPS-only performance during a period with poor GPS satellite geometry in Oslo, February 2, 2010.FIGURE 15. GPS+GLONASS performance during the same period as in Figure 14 in Oslo, February 2, 2010.
Convergence-Time Analysis
As will be shown in the following analysis, adding GLONASS not only improves availability and robustness of the solution, it greatly improves convergence time. Real-time high-accuracy PPP solutions use carrier-phase measurements to achieve high-accuracy positioning. To do so, the carrier-phase ambiguities must be determined. This process takes a certain time depending on the observed satellite geometry and is commonly referred to as cold-start convergence time.
Figure 16 presents a theoretical study of the expected convergence time for a GPS-only compared to a combined GPS+GLONASS solution. The lower graph shows how the expected convergence time varies significantly for a GPS-only solution throughout the day, with a peak of 75 minutes. The combined solution shows much more consistent performance, with expected 50–60 percent average improvement over GPS-only.
FIGURE 16. Theoretical study of expected convergence time with actual GPS-and-GLONASS constellation in view of Oslo on June 26, 2009. Adding GLONASS gives a 50–60 percent theoretical convergence time improvement over GPS-only.
We compare this theoretical study to results using G2 data produced in real time in Figure 17. A cold start is performed every 5 minutes throughout the day, for six consecutive days, giving a total of 1,728 convergence tests. The convergence criterion is the time when the 3D position arrives within 40 centimeters of the reference position and remains there for a minimum of 10 minutes. The average convergence time improvement achieved in Figure 17 is 39 percent, with some variations from day to day. On the better days, the average improvement is almost 50 percent, and close to the expected performance based on the theoretical study. On other days, there is room for further improvement. Mainly two factors are expected to contribute: more and newer GLONASS satellites, and further improvements of the G2 precise GPS and GLONASS orbit and clock product.
FIGURE 17. Convergence results for six consecutive days starting June 24, 2009. Average convergence time of GPS-only is 27 minutes, and GPS+GLONASS is 16.5 minutes, a 39 percent improvement.
Dynamic Environment Results
Since late 2008, the G2 system has been installed on the vessel Bourbon Topaz, making frequent trips into the North Sea and back into port in Norway (see BOX).
All positioning data from both the G2 system and the GPS-only reference systems are logged in real time on the vessel. Figure 18 gives an example plot of the relative height estimated by the G2 GPS-GLONASS solution. In the beginning of the plot, the vessel is out at sea, clearly seen as a noise in the graph that actually is the vessel’s movement in the waves. Then the vessel comes into port and the slower tidal variations are observed for the next 12 hours until the vessel again goes back out to sea.
FIGURE 18. Relative G2 height measurements for a 24 hour period. The vessel is in harbor from 04:00 – 16:00 UTC.
On June 22, 2009, an incident was recorded where the combined GPS-GLONASS G2 solution improves performance. As seen in Figure 19, there is a period starting at 10:00 UTC where the GPS-only reference systems suffer from poorer DOP values, and this is reflected both in horizontal and vertical components of the calculated position. This particular plot shows how the height drifts off by roughly 1 meter while the G2 combined solution remains unaffected for the entire period. Generally, the G2 solution also shows a smoother height than the reference system even when such problems as shown here are not present.
FIGURE 19. Height graph from the Bourbon Topaz while in harbor on June 22, 2009. The GPS-only reference system has a period with poor DOP values while the GPS-plus-GLONASS solution is not affected.
The Bourbon Topaz carries the G2 system on operations in the North Sea, and continuously compares it with the GPS-only reference systems onboard.
Test of G2 onboard Bourbon Topaz
The Bourbon Topaz is a modern supply vessel equipped with the latest dynamic positioning (DP) systems, operating in the North Sea. The North Sea can be a harsh environment in which to operate, and we rely on good tools for maneuvering our vessels.
Early on, we recognized the need for stable, reliable reference systems, and our fleet is equipped with Kongsberg Seatex DPS700 system as standard. When we were asked to test the G2 onboard the Bourbon Topaz, we saw this as an opportunity to follow the development in the industry of such services. The DPS232 receiver was set up in connection with the vessel’s DPS700 system, and all information was logged and sent to Fugro Seastar.
We often experience that the vessel has to operate close to offshore installations, which could block good reception of signals. In these cases, the G2 offers a much better and more reliable signal reception. Our experience of the quality of the G2 system is overall positive.
User Equipment
G2 and the other Fugro services can be received from a variety of different user equipment; both Fugro-branded or manufactured equipment and third-party equipment. In most cases the L-band receiver decoding the data from the geostationary satellites, including Fugro subscription software and position calculation module, is integrated into the same box as the GNSS receiver. Both the GNSS and geostationary satellite signals can be tracked with a single antenna.
FIGURE 20. Receivers supporting the Fugro services. These are only examples, and not all third-party equipment manufacturers are shown. Fugro L-band data reception receiver and positioning/subscription software reside inside the receiver.
Conclusions
Test results confirm decimeter-level position accuracy in real-time navigation with G2, the first real-time combined GPS and GLONASS PPP service. Several examples show how G2 improves availability, robustness, and convergence time compared to GPS-only positioning.
More is better. There is no need to wait for future constellations like Galileo to reap the benefits of access to additional GNSS satellites now.
Tor Melgard is R&D manager at Fugro Seastar in Oslo, Norway. He holds an M.Sc. in electrical engineering from the Norwegian Institute of Technology and wrote his thesis at the Department of Geomatics Engineering, University of Calgary.
Erik Vigen is a senior developer at Fugro Seastar. He received his M.Sc. in Geodesy from the Norwegian Institute of Technology.
Ole Ørpen is senior scientist at Fugro Seastar. He received his M.Sc. from the Norwegian Institute of Technology in electrical engineering.
Jon Helge Ulstein is IT superintendent at Bourbon Offshore Norway AS, a subsidiary of the Bourbon Group, Marseilles, France.
The next-generation GPS ground-control system, known as OCX.
Officials from the Space and Missile Systems Center’s Global Positioning Systems Wing announced today the award of the Next-Generation GPS Control Segment (OCX) contract to Raytheon Company, Intelligence and Information Systems, Aurora, Colorado.
The OCX development contract will be 73 months in duration and with option years for sustainment worth $1,535,147,916. The contract will include development and installation of hardware and software at GPS control stations at Schriever Air Force Base in Colorado and Vandenberg AFB in California, deployment of advanced monitor stations at remote sites, and initial contractor support with sustainment options for five years.
OCX will replace the current GPS Operational Control System, maintaining backwards compatibility with the Block IIR and IIR-M constellation, providing command and control of the new GPS IIF and GPS III families of satellites, and enabling new modernized signal capabilities.
“OCX is urgently needed not only to enable new warfighter capabilities but also to put the new GPS III space vehicles into mission operations,” said Col. Dave Madden, commander, GPSW. “OCX will have a flexible architecture that can rapidly adapt to the changing needs of today’s warfighter and will connect to the Global Information Grid so that warfighters around the globe have immediate access to GPS data and constellation status.”
“OCX will allow AFSPC to effectively and efficiently plan and control full-spectrum precision position, navigation and timing information for all GPS user communities,” Madden said. “OCX will achieve this vision by implementing an incremental development approach that supports the evolving military operational environment, while enabling civil and international users who are employing GPS in innovative applications like transportation.”
The Air Force Space Command’s Space and Missile Systems Center, located at Los Angeles Air Force Base, California, is the U.S. Air Force’s center of acquisition excellence for acquiring and developing military space systems including six wings and three groups responsible for GPS, military satellite communications, defense meteorological satellites, space launch and range systems, satellite control network, space-based infrared systems, intercontinental ballistic missile systems, and space situational awareness capabilities.
By Ruizhi Chen, Heidi Kuusniemi, Yuwei Chen, Ling Pei, Wei Chen, Jingbin Liu, Helena Leppäkoski, Jarmo Takala
Currently, no single technology, system, or sensor can provide a positioning solution any time, anywhere. The key is to utilize multiple technologies. We are now exploring a multi-sensor multi-network (MSMN) approach for a seamless indoor-outdoor solution. Its hardware platform is described in the previous article. The digital signal processor (DSP) is embedded in the GPS module. All sensors are integrated to the DSP that hosts core software for real-time sensor data acquisition and real-time processing to estimate user location. A smartphone handset provides wireless network measurements.
Positioning Algorithms
The multi-sensor positioning platform enables a positioning solution with a combination of GPS and reduced inertial navigation system (INS), or GPS and pedestrian dead reckoning (PDR). The reduced INS consists of a 3D accelerometer and a 2D digital compass, as a low-cost alternative to augment GNSS positioning. The reduced INS combined with GPS uses a loosely coupled Kalman filter for data integration, while the combination of PDR and GPS uses algorithms for estimating the position change with pedestrian step-length estimation.
PDR. The PDR solution uses human physiological characteristics, implemented in a local-level frame, with equations:
where k denotes the current epoch, Y is the coordinate in East direction, X is the coordinate in North direction, S is step length, and φ is the heading.
The PDR positioning algorithm includes step detection, step length estimation, determination of heading, and positioning.
To achieve an accurate heading, compass measurements are corrected with an empirical online estimated error model, which requires some training data.
WLAN and Bluetooth. Figure 1 describes the basic concept of the WLAN or Bluetooth locating solution using a fingerprint database approach. The circles around the access point (AP) in the figure represent the radio coverage area and the color the signal strength. This radio map is a simplified example representing measurements from just one AP.
FIGURE 1. Sample WLAN or Bluetooth fingerprint map, in meters.
For the fingerprinting approach, the received signal strength indicators (RSSIs) are the basic observables. The whole process consists of a training phase and a positioning phase. During the training phase, a radio map of probability distribution of the received signal strength is constructed for the targeted area. The targeted area is divided into a matrix of grids, and the central point of each grid is referred to as a reference point. The probability distribution of the received signal strength at each reference point is represented by a Weibull function, and the parameters of the Weibull function are estimated with the limited number of training observation samples. Based on the constructed radio map, the positioning phase determines the current location using the measured RSSI observations in real time.
Given the observation vector , the problem is to find the most probable location (l) with the maximized conditional probability , maximized by Bayesian theorem as:
We applied an assumption of Hidden Markov Models (HMM) to represent the pedestrian movement process. The locating problem is then translated into finding such a state sequence (locations) that is most likely to have generated the output sequence (the measured RSSIs) assuming the given HMM model. The Viterbi algorithm typically solves these kinds of problems efficiently. This study also utilizes the Viterbi algorithm to trace the user trajectory.
MSMN. The general integration scheme combining the GPS output, sensor measurements, WLAN, or Bluetooth output, and their variance estimates is depicted in Figure 2. A simplified representation of the central filter combining different input sources can be described with typical Kalman filter equations. The measurement model is zk= Hkxk+vk where the state estimate vector is ,
with X, Y, and φ as previously defined, and S the user horizontal velocity (speed). The measurement vector is given as
where g refers to GPS, W to WLAN/Bluetooth, acc to accelerometer, and dc to digital compass. The matrix Hk is the design matrix of the system and the vector vk is the measurement error vector.
FIGURE 2. Integration scheme for multi-sensor, multi-network positioning approach
The recursive sequence includes prediction and update steps. The prediction step includes the typical equations of
and
while the update step includes
Indoor Test Results
A field test has been carried out on a sports field, described in the accompanying article (see Going 3D). An indoor test was carried out in an office-building corridor, but the test started and ended in an outdoor terrace area. During the test, the indoor corridor was covered with eight WLAN and three BT APs.
Figure 3 shows the positioning results of the GPS-only (red), Bluetooth-only (black), and WLAN-only (magenta) solutions; Figure 4 shows that of the integrated multi-sensor multi-network (MSMN) solution (blue) for an outdoor-indoor-outdoor test. A reference trajectory is in green in both figures and building outlines in grey. The position update rate achievable by the WLAN and Bluetooth fingerprinting approach is only 0.1 Hz whereas the GPS-only and the integrated MSMN solutions are obtained every second and thus have a higher availability.
FIGURE 3. Pedestrian test results with GPS-only, BT-only, and WLAN-only positioning approaches with respect to a reference trajectoryFIGURE 4. Pedestrian test result with the multi-sensor multi-network positioning approach with respect to a reference trajectory
Figure 5 shows the horizontal errors obtained with the different positioning solutions over time in the indoor test. A mean horizontal error of 2.2 meters was achieved with the WLAN solution. The Bluetooth solution is not as accurate as the WLAN solution, due to the smaller amount of BT APs; it achieved a mean horizontal error of 5.1 meters. When moving inside the corridor, the GPS solutions are used for the MSMN integration only with very low weights due to their poor quality. GPS is mainly used as a source of location outdoors where the test starts and ends. The mean horizontal error of the GPS-only solutions during the whole test is 8.4 meters. WLAN- and Bluetooth-derived locations and the self-contained sensors are the main sources used inside the building for the MSMN positioning solution: the mean horizontal accuracy o
btained with MSMN is 2.7 meters with a solution availability of 1 Hz.
FIGURE 5. Horizontal errors of GPS-only, BT-only, WLAN-only and the MSMN positioning approaches with respect to time in the pedestrian indoor test
The MSMN solution obviously performs much better than a GPS-only solution indoors. The track of the pedestrian walking inside the corridor can be identified clearly, which is not the case with typical approaches of GPS-only or GPS/low-cost sensors. WLAN fingerprinting provides good position accuracy indoors, but the MSMN solution provides the best result when taking into account positioning accuracy and the solution availabilities in both time and space domains.
Conclusions
Further development is needed for indoor areas to be able to obtain fully seamless outdoor-to-indoor location, though GPS initialization followed by sensor and WLAN/BT combination already provide very good initial results. Additional sensors and more refined pedestrian-specific algorithms will be added to further improve the positioning accuracy.
To enrich user experience of location-based services and personal navigation, three-dimensional models such as those used in urban planning are added to a smartphone platform, without the requirement of additional hardware.
Most current map applications for smartphones and other devices providing location-based services (LBS) are based on two-dimensional maps. Three-dimensional (3D) city models are widely used in applications such as engineering design, environmental modeling, and urban planning. Adapting such models for use in smartphones would make it possible to render 3D scenes in real time, enriching contents and user experience for personal navigation and LBS. A delimited yet large-scale event such as the upcoming 2010 World Exposition in Shanghai provides a promising area for system development and testing.
3D visualization consumes a large amount of computing power, and most of the current successful applications run in a PC environment, as does the Google Earth 3D application. It is still a very challenging task to implement 3D visualization in an embedded system such as a smartphone.
This article presents an entire 3D personal navigation system based on a smartphone platform, the Nokia S60 platform. The study covers the following aspects:
3D personal navigation and LBS service in a smartphone
3D city modelling, and
multi-sensor positioning.
The objectives of the work include prototyping an entire handset-based 3D personal navigation and LBS system utilizing WLAN/Bluetooth positioning technologies, handset built-in GPS/AGPS, and 3D modeling and visualization (basic demonstration scenario), as well as presenting a multi-sensor positioning (MSP) platform in addition to the handset software (advanced demonstration scenario).
3D Personal Navigation and LBS
No additional hardware is added to the Nokia Series 60 (S60) smartphone platform to achieve the 3D visualizations or other functions in the software. Figure 1 demonstrates the functionalities and features available in the 3D viewing of the LBS software. Figure 2 shows the general architecture of the software.
FIGURE 1. Functionalities of the 3D LBS softwareFIGURE 2. General architecture of the 3D personal navigation and LBS software
The software development work focuses on the UI layer, framework layer, and component layer. The software mainly includes the following components:
the 3D visualization engine based on OpenGL ES,
the route plan component,
the locator component,
the LBS client component, and
UI and framework.
Most of the challenging tasks are included in the development of the elements in the component layer, especially in the development of the 3D visualization engine based on the OpenGL ES API that is available from the S60 platform SDK (Software Development Kit). The high-level 3D visualization engine architecture covers the interface layer, the core engine layer, and the data management layer. The first one is responsible for cross-component functional communication, request handling, and data exchange. It provides users with the 3D scene visualization functionalities to access the core engine layer via a single class called NaviSceneControl, which includes all the operations of the 3D visualization: scene zooming, view angle rotating, scene and cursor moving, and selecting route planning and virtual navigation.
The core engine layer takes care of the 3D scene visualization computation and model object management. To enable the 3D visualization for a large region, the objects in the scene are classified into two categories in this layer. One is the 3D models like buildings, trees and poles, while the other is texture of land surface, which consist of ortho-rectified digital aerial photos. All the objects are processed as tiles according to the incoming parameters from the interface layer. Therefore only a small subset is loaded dynamically instead of the whole data.
The data management layer accesses the 3D models and ground-texture images persistent on the flash disk of the mobile phone through an independent thread. To reduce the data size of the 3D models, the original .3ds file created from 3D Max Studio software is compressed to fulfill the requirements of the mobile device.
A simple route plan component is implemented in the software to enable to the user to find and view the route to his or her destination. In order to be able to show the entire route, the calculated route will be displayed on top of a 3D view with a downward camera at a high altitude. The 3D scene in this case looks like an orthoimage. An orthoimage shows objects in the perpendicular view to the projection plane of the objects.
The locator component aggregates the positioning information either from the built-in positioning sensors in the smartphone, a GPS receiver, and a WLAN (Wireless Local Area Network) or a Bluetooth chip, or any external positioning device, such as also the multi-sensor positioning (MSP) device developed in this project. It forwards the positioning information including the location and heading information to the route plan component and the 3D visualization engine to accomplish the navigation functions.
The purpose of the LBS client component in the handset software is to access the LBS server.
Figure 3 shows the overview of the mechanism for delivering the location-based services. The services are classified into two categories: the static services and the dynamic services. The static services include those services that are not changing in time. For example, POIs (points of interest) belong to this category of service. The static services are stored in a database that can be downloaded from the Internet by the users in advance. The users can store the database in the memory card of the phone before running the 3D personal navigation and LBS software. With this approach, it saves the data transmission fee for the end-users when accessing the LBS. The dynamic services cover those services that change in time. For example, a piece of real-time news is one of the typical dynamic LBS. For accessing the dynamic LBS, the Really Simple Syndication (RSS) technology is adapted in our implementation.
FIGURE 3. Mechanism for delivering location-based services and information
The LBS client component is implemented so that the handset will pull automatically the news in the background in real time via a widget reader embedded in the LBS client component. Whenever new information is uploaded to the LBS server or to the registered web pages, mobile users will be notified.
In addition to RSS technology, another approach to broadcast LBS information is considered in the system: to disseminate the LBS information via an SBAS (satellite-based augmentation system) pseudolite. The dynamic LBS information (e.g., a short message) can be first encoded into a user-defined SBAS message. The message encoded is then sent to a pseudolite from which the message is broadcast. The corresponding SBAS message can, in fact, be received by any SBAS-enabled receiver located within radio coverage area of the pseudolite. However, the encoded LBS message can be decoded only with the receiver that has a special firmware, developed in this case by the Finnish Geodetic Institute (FGI). Having received and decoded the LBS messages transmitted from the pseudolite with a dedicated receiver, for example the MSP device part of the more advanced demonstration scenario of the project, the content of the message is then encoded to a user-defined NMEA (National Marine Electronics Association) message and transmitted to a mobile phone in the vicinity via a Bluetooth connection as shown in Figure 3. This solution of LBS data distribution is available only to a very limited number of users with receivers carting a special firmware developed by FGI.
3D City Modeling
Due to the memory limitations of a mobile phone, there are certain requirements for the 3D models applied. In our study, a test scene for model reconstruction is focused on a street in Espoo, Finland, in an ordinary residential area. A vehicle-borne mobile mapping system ROAMER (see photo) developed by FGI performed the data acquisition. It consists of a carrying platform, a positioning and navigation system, and a 3D laser scanner system. With the ROAMER system, visible objects can be measured with an accuracy of a few decimeters with a maximum vehicle speed of 50–60 km/hour, and the data for the desired objects can be collected within the range of several tens of meters.
ROAMER vehicle-based mobile mapping system.
A large amount of data is produced from the system, and noise and outlier points are needed to be removed. Valid data is classified into different point groups using an automatic algorithm developed by FGI. These point groups include buildings, trees, roads, and poles. Models are then reconstructed based on these classified point groups.
Modeling methods are developed to meet the application requirements of personal navigation: small model size, high accuracy, and good visual appearance. Small model size is achieved by simplified object geometry and reduced texture resolution. Model accuracy is controled by extracting building outlines from a classified point cloud and overlapping with the final 3D model. The model completeness is checked by comparing the resulting model with original images. Good visual effect is realized by applying photo-realistic texture. Photo-realistic texture provides rich information for the 3D scene reconstructed. Figure 4 presents the total process of the 3D modeling, in which only the individual object texture and the final model constructions require manual editing. Figure 5 shows the raw data retrieved and Figure 6 presents the final 3D models of the test area.
FIGURE 4. The process of 3D modelingFIGURE 5. Raw data retrieved from the test area with FGI’s ROAMER systemFIGURE 6. Reconstructed 3D scene of the test area
To import the final 3D models to a mobile phone, the size of an individual model is restricted to less than 100 kb. To optimize model size, a row of buildings is divided into several building blocks.
Multi-Sensor Positioning
As long as open-sky satellite-signal conditions are available, there are no problems to locate a mobile user with the built-in GPS receiver of a smartphone with a positioning accuracy of a few meters. However, most popular location-based services occur in GNSS-degraded environments such as in indoor environments and urban canyons. Locating a mobile user seamlessly any time anywhere under any circumstance is still a very challenging task, especially to implement such an indoor/outdoor positioning solution in a digital signal processor (DSP) platform.
FGI is now developing a DSP-based multi-sensor positioning platform to approach a seamless indoor/outdoor locating solution. The platform consists of a GPS module, a 3D accelerometer, and a 2D digital compass (Figure 7). A DSP is embedded in the GPS module. All sensors are integrated to the DSP that hosts a core software for real-time sensor data acquisition and real-time processing to estimate user’s location.
FIGURE 7. Hardware platform
The multi-sensor platform provides opportunities to investigate the positioning solutions with a GPS/Reduced-INS (Inertial Navigation System) combination or GPS/PDR (Pedestrian Dead Reckoning) combination. The Reduced-INS combination is defined as a combination of a 3D accelerometer and a 2D digital compass, and is a very low-cost approach of sensor augmentation. The GPS/Reduced-INS implementation is implemented in a loosely coupled Kalman filter, while the GPS/PDR algorithm is based on pedestrian-targeted dead reckoning, with heading error and step length estimation methodology.
Preliminary tests analyzing both GPS/Reduced-INS and GPS/PDR solutions have been carried out in a sports field on a 400-meter running track. In order to simulate a GPS outage situation, the GPS measurements were ignored for one minute. During this one minute “outage,” the traveling trajectories are estimated with the Reduced-INS solution and the PDR solution. Figure 8 shows the trajectory of the Reduced-INS solution, while Figure 9 shows that of the PDR solution.
FIGURE 8. Trajectory estimation for the 1-minute GPS outage using Reduced-INS approachFIGURE 9. Trajectory estimation for the 1-minute GPS outage using the PDR approach
The Reduced-INS approach provides a reasonable result with a positioning accuracy of about 20 meters at the end of the forced 1-minute GPS outage. The PDR approach provides a better prediction in this case, resulting in only a couple of meters of error after the 1-minute outage of absolute location input from the GPS, because the heading errors are modeled carefully utilizing previous training with data from a previous run along the same track as well as accurate step detection estimation.
Conclusions
The prototype system will be tested and demonstrated at the 2010 World Expo in Shanghai, implemented with a smartphone software package: anyone with a Nokia phone (S60 with built-in GPS and WLAN/BT) can experience the 3D personal navigation and LBS service in the Expo area by downloading and installing the 3D models. The prototype has so far met these challenges: the high performance required of real-time 3D visualization in a smartphone; high positioning availability with acceptable accuracy in indoor and outdoor environments; and the demanding requirements of the 3D models for a small phone, including small model size, high accuracy, and good visual appearance.
Manufacturers
The multi-sensor positioning platform consists of a Fastrax iTrax03 GPS module, a VTI SCA3000-D1 3D accelerometer, and a Honeywell HMC6352 2D digital compass. The ROAMER mobile mapping system consists of a Faro LS 880HE80 terrestrial laser scanner, two AVT Oscar F-810C cameras, and a NovAtel SPAN geo-reference system.
RUIZHI CHEN is a professor and head of the Department of Navigation and Positioning at the Finnish Geodetic Institute, where Heidi Kuusniemi is a specialist research scientist, Juha Hyyppä is a professor and head of the Department of Remote Sensing and Photogrammetry, Risto Kuittinen is director general, Yuwei Chen is a specialist research scientist, and Ling Pei, Lingli Zhu and Jingbin Liu are senior research scientists.
JIXIAN ZHANG is a professor and president of the Chinese Academy of Surveying and Mapping, where Yan Qin and Zhengjun Liu also work as the director of the Department of Research and Development and the group leader in the Institute of Photogrammetry and Remote Sensing, respectively.
JARMO TAKALA is a professor and head of the Department of Computer Systems at Tampere University of Technology in Finland, where Helena Leppäkoski is a researcher.
JIANYU WANG is a professor at the Shanghai Institute of Technical Physics, Chinese Academy of Sciences.
Deterministic risk modeling, the basis of the Efficient Market Hypothesis (EMH) at the core of modern quantitative finance, is known to be fundamentally flawed, but its elegance and convenience has blinded researchers to growing evidence of its weaknesses. The near-complete acceptance of the EMH led to models that dramatically accentuate its flaws, which in turn led to absurd but eagerly accepted conclusions for loan-default risk. These models proved dramatically vulnerable to changes in the housing market in 2007–2008 and led directly to the ensuing crash.
The gross inattention to potential anomalies and violations of nominal behavior that characterize quantitative finance fortunately do not apply to satellite navigation integrity assurance. Similar techniques and probability distributions are used, but understanding what can go wrong leads to detailed emphasis on modeling and mitigating rare events. Where significant uncertainty exists, conservative assumptions try to be robust to it. Thus, certification of satellite- and ground-based augmentation systems (SBAS and GBAS) likely demonstrates that these systems meet their integrity risk requirements with substantial margin.
Despite this, the predominant use of deterministic models for risk assessment is dangerous because it purports to provide guaranteed bounds on uncertainty that do not apply in practice. The conservative nature of satellite navigation risk assessment greatly reduces but cannot eliminate the underlying integrity risk, while it leads to performance losses with potentially unmeasured safety impacts. Given the uncertainty that is present, probabilistic models are much better suited to providing “illusion-free” risk assessments that enable realistic system-level design trade-offs.
Economics. Decades of financial theory are based upon the assumption that the normal (Gaussian) distribution applies to financial markets. In spite of common-sense arguments to the contrary, assuming that it does is too convenient to give up, and the theories it gives rise to are so useful that it was thought better to force-fit the model to financial processes. Academic and professional preference for tractable, analytical, easy-to-use models trumped the search for truth.
The simplification of correlation into a single parameter made it easier to fit historical data on mortgage default risk correlation to a tractable model. Despite this, the relative rarity of defaults prior to 2000 made any correlation model based on historical default data highly uncertain. An EMH-based market-driven model for default risk correlation became instantly popular, enabling the creation of complicated mortgage-backed derivatives without in-depth analysis.
The simplicity of the Value-at-Risk output that encouraged its widespread use in corporte risk assessment allowed managers to forget that it was only useful to, at most, the 99th percentile. It quickly became thought of as an actual worst-case bound on losses and treated as such in portfolio optimization. Loss reserves throughout the economy fell far short of what was needed. In retrospect, such approaches that oversimplify risk to the point where managers think they fully understand it are worse than useless, as they are so likely to be abused. Experts should understand risk in all its complexity and communicate that risk to decision-makers as fully as possible.
Mathematics. This financial experience suggests that, as Albert Einstein said, “As long as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.”
Deterministic models provide precise quantification of uncertainty whose accuracy and precision are illusory because they depend wholly on the assumptions used to generate the results. Probabilistic models also produce imprecise outputs, but the imprecision is real, and the goal of these models is to identify this lack of precision, rather than cover it up.
Because the probabilistic approach is so philosophically different from the deterministic one, it is likely that more traditional deterministic risk models will remain dominant. These require multiple assumptions regarding uncertain behavior and simplifications to make the resulting model tractable and useful for analysis. Danger lies in forgetting how these models were created and growing to believe in them too strongly while ignoring all contrary data, as happened with the EMH.
To avoid this, assumptions and simplifications on which deterministic risk models are based should be highlighted not only during the modeling process but also when results are presented. If these shaky foundations are consistently emphasized, fewer people will be tempted to willfully or accidentally misinterpret the results, and researchers will be less likely to extrapolate from one flawed model to another.
Lessons for SatNav Integrity
We must first recognize that integrity or safety assurance for satellite navigation is a unique application of risk assessment in which the aim is to protect passengers from the consequences of very rare but potentially hazardous threats. As in the financial world, the Gaussian probability distribution is used extensively to model nominal error behavior and to compute position-domain protection levels intended to bound worst-case user position errors at the integrity-risk probabilities required for user safety. The Gaussian model is a convenient, efficient means to communicate ground-system errors to SBAS and GBAS users in a single parameter: the standard deviation (or sigma) of range-domain errors.
Great care is taken in using the tails of the Gaussian assumption to bound rare-event errors under nominal conditions (so-called rare-normal errors). Extensive studies of GPS, SBAS, and GBAS data show that, while the Gaussian distribution approximately holds in many cases and is usually a good model within the 99th percentile of errors, it is not a good description of rare-event behavior. In particular, rare-event tails of actual data often considerably exceed what is predicted by the Gaussian distribution. Several reasons exist, but the dominant one is the phenomenon of mixing of errors with different underlying actual distributions. This makes sense: rare-normal errors are not really normal but are instead combinations of off-nominal conditions that have different causes.
Because use of the Gaussian distribution is built into the SBAS and GBAS standards, the primary defense against its inapplicability at low probabilities is to inflate the sigmas broadcast by SBAS or GBAS (or assumed in user equipment) such that the assumed distribution overbounds the actual, unknown (and likely very complex) error distribution at the probabilities that matter for user safety. This is a difficult problem. No matter what approach to deriving bounding inflation factors from collected data is used, no means of proving rare-event error bounding by Gaussian distributions exists or can exist, given that the required assumptions cannot be proven. Despite this, conservatism and common sense in deriving inflation factors (and then applying additional margin for “unknown unknowns”) should sufficiently cover the underlying uncertainty.
Even after inflation has been applied, reliance on Gaussian error models becomes much more critical when they are extrapolated to derive distributions for squares of errors, as is done in receiver autonomous integrity monitoring (RAIM) and in real-time monitoring of the broadcast sigma parameters. Errors in the Gaussian error model are greatly magnified when squared and then assumed to follow a chi-square distribution.
History. Using GPS performance to build models of failure probabilities and anomaly behaviors suffers from a lack of data since GPS was not fully commissioned until 1995. Estimating the prior probability of sudden, unpredictable failures in GPS satellites is mostly based upon the observed failure history of GPS satellites in orbit — but such failures are quite rare and are not consistent across all satellites. They occur more frequently as satellites approach end-of-life, and they change as different satellite blocks deploy over time. There is no guarantee that future satellite or Operational Control Segment performance will correspond to that observed in the past. It is risky to estimate one failure rate across all satellites.
For SBAS and GBAS, conservatism and common sense must again be applied to limit the impact of these uncertainties. Failure-rate estimates are made from data where different satellites are combined, but significant margin is applied to account for differences among satellites. The resulting prior probabilities for failures are conservative for all fault types and extremely conservative for faults where limited or no data exists. The problems of relying on limited historical data are even more severe when threat models are created to represent possible system behaviors when a particular fault or anomaly (for example, satellite signal deformation, ionospheric storms) occurs. In the case of satellite signal deformation, deterministic threat models have been extrapolated from a single observed event, the fault on SVN19 discovered in 1993.
Errors and Failures. The problem of modeling uncertain and potentially time-changing correlations breaks down into error correlation and anomaly correlation. Correlation among nominal errors is relatively easy to deal with because significant data exists; one does not have to wait for anomalous conditions. However, even when truly uncorrelated data is present, the statistical noise inherent in correlation coefficients estimated from data is almost always non-zero. Since the designer cannot tell whether real correlation exists or not, the resulting error sigmas must conservatively allow for significant non-zero correlations.
In GBAS, ground-system reference-receiver antennas are sited far enough apart (100–200 meters) that diffuse multipath (and most specular multipath) should be statistically independent from receiver to receiver. However, this cannot be guaranteed, and even if it is true at a given site, statistical correlation estimates will be non-zero. Therefore, the assumption that nominal error sigmas in the resulting pseudo-range corrections are reduced by a factor of two when averaging measurements across four reference receivers is not strictly valid. Conservative handling of the estimated correlation at a given site can properly de-weight the assumed credit given for averaging, or the designer can choose to take no averaging credit at all.
On the other hand, modeling correlations among rare-event anomalies is very difficult. GNSS satellite failure correlations are hard to foresee because of our limited understanding of their causes. The temptation to ignore correlations and to treat all failures as statistically independent is very high, as this allows the use of simplified probability models and produces probabilities of multiple failures that are usually small enough to be ignored.
This dangerous trap can lead to neglecting important sources of integrity risk. Avoiding it requires assuming some non-zero degree of failure correlation, but without detailed failure cause-and-effect information, it is very difficult to know how much correlation is sufficiently conservative in a deterministic risk model. Here, probabilistic models are far superior, as our degree of uncertainty regarding actual failure correlations can be handled directly by representing different correlation scenarios, or possible states of reality, and assigning probability weights (themselves random variables) to each.
Worst Case. Since the uncertainty inherent in the development of deterministic failure models is well understood, the resulting threat models are usually applied in terms of the worst-case fault within the bounds of the threat model. Once one agrees to ignore the possibility of faults exceeding the threat-model bounds, this worst-case-fault assumption is the most conservative one possible. The worst-case fault is judged from the user’s point of view rather than that of the GNSS or service provider. For example, the worst-case C/A-code signal-deformation on a GPS satellite depends upon the design of the reference receiver providing differential corrections (if any) and the design of the user receiver. SBAS and GBAS users are allowed a pre-specified receiver design space. Given the reference receiver chosen by a given SBAS or GBAS installation, finding the worst-case signal-deformation fault requires error maximization over all possible deformations in the threat model and all possible user receiver design parameters.
Another class of anomalies, large ionospheric spatial gradients, can be used to illustrate this procedure. Figure 1 shows a simplified, linear model of a large, wedge-shaped ionospheric spatial gradient affecting a GBAS installation, and Figure 2 shows a graphical summary of the parameter bounds of the associated threat model developed for the FAA LAAS based on CONUS data. The geometry assumed in Figure 1 is a simplification of reality and cannot be assumed to hold precisely, even though the threat model assumes that it does. Fortunately, the resulting risk assessment is not very sensitive to small deviations from a perfectly linear front slope. This kind of sensitivity analysis is required to test our vulnerability to violations of deterministic models whose underlying assumptions cannot be verified.
FIGURE 1. Geometry of GBAS (LAAS) ionospheric threat model
The parameter bounds in Figure 2 cover the worst validated ionospheric gradients observed since 1999. They cannot be guaranteed to cover future anomalies; thus, ongoing monitoring of ionospheric anomalies is required to see if these bounds need updating in the future. However, the outer bounds of the existing threat model appear to be very conservative because they are driven by a single ionospheric storm on a single day (20 November 2003) in a small region (northern Ohio). This storm appears much worse than the other observations shown in Figure 2. The vast majority of anomalous gradients discovered, most of which are not shown in Figure 2, have slopes under 200 millimeters/kilometer (mm/km) and are generally not threatening to GBAS users.
FIGURE 2. Parameter bounds on GBAS (LAAS) ionospheric threat model for continental United States (CONUS
Therefore, in a probabilistic model, the vast majority of the weighting (given that an anomaly condition exists) would go toward non-threatening gradients with tolerable slopes, a small fraction would go to the 200–300 mm/km slope range, a much smaller fraction to the 300–425 mm/km range, and then a very small but non-zero fraction to gradients above 425 mm/km (the upper bound in Figure 2) that have not been observed to date but cannot be ruled out.
Given this uncertainty within a deterministic model, the worst-case gradient of 425 mm/km (for high-elevation satellites) is assumed to be present at all times, and its hypothetical presence is simulated, with the worst possible approach geometry and timing relative to a single approaching aircraft, on all pairs of satellites otherwise approved by a LAAS ground facility (LGF). The largest resulting vertical position error over all potential user satellite geometries represents the maximum ionospheric error in vertical position (MIEV) that must be protected against. Before mitigation by LGF geometry screening, this worst-case error can be as large as 40–45 meters.
Figure 3 illustrates the potential magnitude of vertical errors under near-worst-cas
e ionospheric anomaly conditions based on a limited probabilistic model that varies front slope (above 350 mm/km), speed, satellites impacted, and approach direction relative to that of the aircraft for a user approaching the LAAS facility at Memphis International Airport with the SPS-standard 24-satellite GPS constellation (only subset geometries with two or fewer satellites removed are considered). The worst-case position error, or MIEV, prior to LGF geometry screening is about 41 meters, but the relative likelihood of this result is very low. Much more common are errors in the 5–15 meter range. This figure does not show the majority of cases where the LGF detects the anomaly before any error occurs. LGF geometry screening acts to remove potential subset geometries (make them unavailable by inflating the broadcast parameters) whose worst-case error exceeds 28.8 meters, but the price of this is substantially lower availability for CAT I precision approaches.
FIGURE 3. Near-worst-case ionosphere-induced vertical position errors at Memphis
Figure 3 shows the extreme level of conservatism that typically results from deterministic worst-case threat model impact analysis. This level of conservatism is so great that it is hard to imagine that the actual user integrity risk is somehow worse than what is modeled in this manner. However, “hard to imagine” does not equate to “is guaranteed not to happen.” The goal of worst-case analysis is to eliminate uncertainty (by assuming the worst possible outcome of the uncertain variables) and thus prove that a given probabilistic integrity risk requirement is met. However, the limited knowledge upon which threat models are based means that such proof is illusory at best and dangerously misleading at worst. Meanwhile, a great deal of performance (in terms of user availability and continuity) is sacrificed. As shown by the example in Figure 3, probabilistic analysis makes it possible to trade off risk reduction and performance benefit in a coordinated manner. The illusion of guaranteed bounds on risk is abandoned, but as the financial crisis illustrates, it is just that — an illusion.
SAM PULLEN is a senior research engineer at Stanford University, where he is the director of the Local Area Augmentation System (LAAS) research effort. He has a Ph.D. from Stanford in aeronautics and astronautics. This article passes quickly over economic details included in his ION-GNSS 2009 paper, “Providing Integrity for Satellite Navigation: Leassons Learned (thus far) from the Financial Collapse.”
The Leica Builder Series Total Stations, just introduced by Leica Geosystems, is designed for construction contractors or anyone on a construction site requiring an easy-to-operate, full-feature measuring tool.
From simple tasks to professional all-day use, the Leica Builder offers a scaled product family of five different models to meet the varying needs of most construction jobs, Leica said. Contractors can choose from the 100-, 200-, 300-, 400-, or 500-series models.
The Leica Builder Series is designed for non-technical construction professional to easily perform positioning, layout, or dozens of other daily construction-site tasks. To facilitate data transfer, some models feature a USB port-an industry first-while the 500 Series also has Bluetooth functionality.
The Leica Builder Series offers the choice of two operation modes. Contractors can operate in stand-alone mode via the on-board PowerSite construction programs with the memory capabilities to collect, store and download data. Alternatively, an application is available for contractors who prefer to use a data collector. The Builder can be coupled with the Leica DX-10 data collector and Siteforeman software.
On-board PowerSite software is designed specifically for construction applications, eliminating surveying features that contractors may not need, Leica said. Contractors do not require a surveying background in order to use the PowerSite software.
Intended for the construction site, the Leica Builder is designed to withstand dust, water, and other environmental conditions with an IP55 rating.
The entry model Leica Builder 100-Series is an electronic digital theodolite for horizontal and vertical angle measurements. Other models — Leica Builder 200, 300, 400, and 500 — are total stations available with reflectorless and/or prism measurement capabilities, data storage via USB memory stick, and wireless communication for more useful functionality. All instruments are equipped with theft protection and support many different languages of which three can be uploaded and switched at the push of a button. The Leica Builder is delivered packaged in a crush-proof transport case with a set of accessories for out-of-the-box use.
The Russian space agency Roscosmos launched a venerable Proton rocket carrying three GLONASS-M satellites into orbit on December 14. Each 3,000-pound satellite is designed to last seven years. They join a constellation numbering 19 satellites, although only 16 are healthy.
Russian politicians and satnav system managers had hoped to launch six satellites between September and December, to attain a global service level, which requires 24 satellites, eight each in three orbital planes.
However, a payload glitch found aboard one recent satellite after its launch into space forced a return to the factory of three satellites scheduled for launch in September. The three put into orbit this week will now only bolster continuing GLONASS coverage of Russian sovereign territory, which requires 18 operating spacecraft.
The next GLONASS launch is now scheduled for a February 11–20, 2010, window.
The Block 41 GLONASS-M satellites (Nos. 30, 33, and 34) have been placed in Plane 1, which currently has only four healthy satellites. According to Roscosmos, communication has been established with all of the satellites and performance is nominal.
Next Up. Nikolay Testoedov, head of the Reshetnev satellite manufacturing company, said his enterprise plans to produce 17 more GLONASS-M satellites between now and 2013.
“The preproduction flight tests of new series of GLONASS satellites, GLONASS-K, will start in 2011,” said Andrei Buravin, vice head of Russian Institute of Space Device Engineering. The preproduction flight tests of GLONASS-K will be performed together with Reshetnev company.
It is still unclear whether the next-generation of GLONASS satellites will be launched via blocks of three satellites with Proton rockets from Baikonur, or via blocks of two satellites with Soyuz rockets from Plesetsk.
RTCM Supports Loran
It may be moot by the time you read this — the U.S. Coast Guard (USCG) could unplug Loran on January 4 — but the Radio Technical Commission for Maritime Services (RTCM) wrote to Secretary of Homeland Security Janet Napolitano in support of continuing and enhancing Loran service.
The letter asserts that it cannot be accurately certified that termination of the operation of the Loran-C signal will not adversely affect the safety of maritime navigation — counter to opinion issued by the USCG Commandant. The RTCM president states that the Loran-C infrastructure is needed to complete the eLoran system to serve as a backup to the U.S. Global Positioning System (GPS).
New Technique. Researchers have developed a technique to demonstrate a low-cost backward-compatible way to exploit eLoran to make GPS more robust. The method paves a way for the average GPS user to become a GPS+eLoran user. Go to env-gpsworld-integration.kinsta.cloud/loran for the letter and other Loran stories.
Galileo Contract Award Imminent
A contract award for at least eight of the in-orbit validation satellites had been promised for the end of this year by the European Commission (EC), but as this magazine goes to press on December 16, no official announcement has surfaced.
An unconfirmed report in early December claimed that the European Commission and European Space Agency had awarded a contract for eight Galileo satellites to underdog bidder OHB Technology of Germany. However, this report was privately denied and in fact refuted by an EC representative.
The OHB-led consortium includes small-satellite specialist Surrey Satellite Technology Ltd. of Britain, which built and continues to operate the GIOVE-A satellite, Galileo’s first launch. The competing Astrium-Thales Alenia consortium built the second Galileo satellite now in orbit, GIOVE-B.
The report, published on December 4 on the Space News website, asserted that “the European Commission has selected OHB Technology of Germany to build at least eight Galileo navigation and positioning satellites for about 350 million euros ($525 million) in a decision that postpones any award to competitor Astrium Satellites pending further negotiations with Astrium.” Reporter Peter de Selding cites industry officials as his sources.
An EC representative privately denied the report, asserting “it is not true.” An industry source said “It is not confirmed, we are waiting for the decision.”
The rumor created an uproar in the German state of Bavaria, a center for that country’s aerospace industry and government-aided research. Astrium had reportedly planned to perform much of its Galileo work in that region, and the Space News story holds out the expectation that “political pressure will be applied to reverse the ruling in the coming days.” The region is already home to the Galileo Control Center at a German Aerospace Agency (DLR) site.
Block Approach. The two consortia have been negotiating their bids on the contract with the commission and its technical adviser, the European Space Agency (ESA), for 15 months. Initially, the two European Union bodies set a contract ceiling of 840 million euros to build 28 Galileo satellites; un the past few months they revised the total order to 22 satellites and asked for bids for eight, 14, and 22 satellites. Reportedly, there are price ceilings for each of the three potential order sizes — around 400 million euros for eight satellites, 650 million euros for 14 satellites, and 840 million euros for all 22.
Repeatedly postponed throughout its conceptual phase, the Galileo system now — officially, at least — hopes to achieve initial operational capability by 2014.
Whether or not the Space News report is eventually substantiated, the central European government has already signaled in multiple ways its dissatisfaction with its various member states’ aerospace industry giants, whom it holds responsible for the protracted dysfunctionality of the now-abandoned public-private partnership to build Galileo. The EC has largely wrested control of the satellite award process away from its space agency, and indicated that it intends to maintain a firm grip on the purse strings.
Application Days: Galileo Application Days are set for March 3–5, 2010, in Brussels, Belgium, with live demonstrations of cutting-edge applications developed for GNSS under the European Union’s 7th Research Framework Programme (FP7), former ESNC Competitions, the ESA Technology Transfer Programme, and national and regional initiatives. See www.application-days.eu for details.
Roughly three years ago, the U.S. military conducted the first flex-power test on the L2 GPS codeless signal. Almost immediately, the civilian GPS community expressed concern that future changes to the L2P(Y) signal power levels might cause a signal phase shift; such a phase shift would be incompatible with equipment using the P(Y) signals in a codeless/semicodeless fashion for extremely accurate positioning applications.
Civilian users were naturally upset because they had invested millions of dollars in systems that might not be usable — even if the unusable periods were of a very short duration.
The National Positioning, Navigation, and Timing (PNT) Executive Committee responded by tasking the National PNT Engineering Forum (NPEF) to look at the problem. Within a few months, the NPEF announced a solution: flex power could be used in such a manner that it would not cause a phase shift. At the same time, the military reminded civilian users that the codeless use of L2P(Y), as accurate as it might be, was never intended and should not be a long-term solution.
An agreement was reached between the U.S. government and civilian users that the civilian users of this codeless/semicodeless technique would migrate from using the L2P(Y) carrier to using the new L2C signal to achieve not only the same, but better results. To codify this agreement, a Federal Register Notice was issued in 2008 identifying the terms of this agreement, which guaranteed the phase stability of the current L2P(Y) signal until 2020. This gives civilian users 12 years to figure out a migration plan and to obtain adequate use of the equipment they already have on hand.
In addition, 2020 is not a drop-dead date, but a date when the use of L2P(Y) codeless signals will no longer be guaranteed, though may well still work. Who knows what PNT advancements will take place between now and then? This could very well be a moot point by then, and in my opinion should be one now.
Problem Solved? Apparently not. A lag between the issuance of this national policy and analogous adjustments to interface specifications caused consternation within the civilian community. Misunderstandings added to this perceived impasse. Various solutions were identified to work around this looming quandary. However, given the national policy to support codeless/semicodeless use until 2020, the Air Force Space Command commitment to that policy, and the recommendations of the NPEF, these solutions seem wholly unnecessary to me.
The U.S. government has gone well beyond what is required to insure civilian codeless and semi-codeless users are accommodated.
For the foreseeable future, users will be able to employ L2P(Y) codeless/semicodeless techniques for very accurate position determination and will not have to worry about phase shifts disrupting their work.
— Don Jewell, GPS World Defense PNT Contributing Editor
A team will attempt to shatter the world land speed record, with a GPS/GLONASS receiver riding the controls.
photo: GPS/GLONASS
In summer 2010, a team of 44 volunteers will attempt to shatter the world land speed record of 763 miles per hour (2 mph faster than sound) by hitting 800 on the speedometer. To ensure that the North American Eagle takes it to the limit efficiently and safely, the team captures performance data from 70 sensors, locked to position, velocity, and time coordinates by an onboard GNSS receiver.
An old Lockheed F-104, a ’60s era Mach II fighter, rescued from a scrap dealer in Maine seemed to have the mark of greatness somehow still on it, amid the fuselage holes and grafitti. A team of volunteers converted the plane into a supersonic car: one expert machined the solid billet aluminum wheels, another rebuilt the General Electric J-79 engine, another devised a magnetic braking system. Over three years, the team replaced 40 percent of the plane’s skin panels, 5,000 rivets, the front suspension, and the steering and hydraulics systems.
The 56-feet-long, 13,000-pound car features Magna Force Lev-X magnetic brakes, a stock engine that outputs 42,500 horsepower, burning 160 gallons of fuel per minute in afterburner mode, and a backup 52,000-hp engine.
At supersonic speeds, anticipating and controlling the car’s reactions to physical stresses become critical. explains Steve Wallace, data-acquisition engineer: “Stuff happens when you get up to the speed of sound. Shock waves affect the aerodynamic balance of the vehicle, and when you’re flying six inches from the ground, aerodynamic unbalance becomes very important.” Wallace and the team must ensure that the car does not burrow into the ground or lift any of its wheels while in motion; either would be detrimental to record-setting and driver safety.
Accelerometers, strain gauges, piezoelectric sensors, inertial gyros, airspeed and air-pressure sensors, and more all generate streams of data, stored on a laptop computer mounted behind the car’s cockpit. Wallace accesses the system in real time via an Ethernet wireless umbrella of routers mounted on 20-foot-tall towers spaced at 2½-mile intervals alongside a 14-mile track at Black Rock Desert, Nevada. After a test run, he downloads the data to his computer, correlates time and location, then exports to a spreadsheet.
GNSS makes speed measurement more reliable and accurate. “We have a lot of sensors, but one sensor I don’t have is for vehicle speed,” he says. “This is a thrust vehicle; the wheels aren’t driven, so realistically, the wheels are never going as fast as the ground. Measuring airspeed is not a bad way of [measuring vehicle speed], but it’s very noisy. The data are all over the place and you have to do a lot of smoothing and averaging. With GNSS, it’s dead nuts — a great way of getting information I need, fast, without looking at accelerometer data. I can’t think of a more valuable tool to understand what’s happening from a sense of motion of the vehicle in general.
“If we had a $10 million budget, we could buy a ground-tracking radar unit like the Air Force uses, but that ain’t gonna happen. We can get just as good data with this as with a $10 million system, so why go any further?”
The project could provide further practical engineering benefits. The team likens this research to that of the 1960s space program, which benefited development of computers, cellular phones, and microwave ovens.
Manufacturers
A dual-frequency Topcon PG-A1 antenna and Euro 160T OEM receiver collect GPS/GLONASS signals at 20 Hz. A Euro 160T mobile control board rides in the car’s electronics bay. A GB-1000 receiver collects static reference data.
Nice article on “The Smartphone Revolution” in the December issue. I am not so tech-y about the working of GPS/indoor GPS, but I am interested more in this technology specific to indoor GPS (repeaters).
Can the smartphones get indoor GPS signals correctly and quickly? If the smart phones are really smart that they can connect to GPS satellite from indoor locations, then do the GPS repeater products become obsolete?
— Saad B.
Author Frank van Diggelen replies:
The simple answer to your first question is yes, many of these smartphones can get GPS signals indoors. But indoors is a big and varied place, and the more complete answer is that the term “indoor GPS,” like “offroad vehicle,” describes the presence of a capability, not the absence of all limitations. So even if your GPS receiver works indoors in some locations, there will always be other places it doesn’t. And it will generally work better where there are stronger signals, like outdoors.
Similarly, for your second question, high-sensitivity GPS will work some places indoors, but not everywhere, so there is a role for repeaters. However, GPS repeaters are like a long cable from the repeater’s receiving GPS antenna; so any GPS receiver that gets signals from a repeater will compute the position of the repeater’s receiving GPS antenna.
Nice article. One comment and one question.
Comment: The IGS ultra-rapids that started in 1999–2000 were from the beginning available for the future. They always contained 24 hours of estimated orbits and 24 hours of predicted orbits usable in real time. As I was responsible for generating these products within the IGS at that time, I am pretty sure that was the case.
Question: I do not understand why you write that turning off SA (Seletive Availability) was an enabler for A-GPS!? I know that one possible feature of SA was an artificial degradation of the satellite ephemerides but this option was never exercised to my knowledge. So using a global network to obtain broadcast ephemerides and predict them into the future was always possible. Nothing fundamentally changed when SA was turned off!?
— Tim Springer
Frank van Diggelen replies:
Just to be clear, the article enumerated seven enabling technologies for the revolution of GPS in cell phones (they are: A-GPS; massive parallel correlation; high sensitivity; coarse-time navigation; low-power TOW decoding; host-based GPS; and RF-CMOS), and a dominant spin-off technology: long-term orbits good for many days into the future.
The demise of SA made it easier to predict long-term orbits for two reasons: technical and commercial.
Technically, if you used the code- or carrier-phase measurements in your orbit modeling, then it was easier if these measurements were not degraded. Of course, you could have corrected them differentially, but the article makes the point that things were harder (not impossible) when SA was on.
On the other hand, if all you did was use the broadcast ephemeris in your predictions, then as you suggest nothing changed, technically, if the ephemeris degradation option was not exercised. But the fact that this degradation option existed made it a more difficult commercial proposition to develop a system for predicting many days of ephemeris. Thus the end of SA certainly helped facilitate the commercial availability of predicted ephemeris that is valid for many days into the future.
However, a payload glitch found aboard one recent satellite after its launch into space forced a return to the factory of three satellites scheduled for launch in September. The three put into orbit this week will now only bolster continuing GLONASS coverage of Russian sovereign territory, which requires 18 operating spacecraft.
