GNSS researchers presented hundreds of papers at the 2022 Institute of Navigation (ION) GNSS+ conference, which took place Sept. 19-23, 2022, in Denver, Colorado, and virtually. The following four papers focused on the use of GNSS in urban environments. The papers are available here.
GPS World will be attending this year’s ION conference in Denver, Colorado, on Sept. 11-15.
FGO-based GNSS/INS integration improves performance in urban canyons in Hong Kong
The integration of GNSS and inertial navigation systems (INS) has the potential to improve performance due to their complementariness. In this paper, the authors investigated positioning based on the integration of GNSS and INS using factor graph optimization (FGO). This ultimately showed improved performance in urban canyons in Hong Kong. The effectiveness of the proposed method was verified using challenging datasets collected using two automobile-level GNSS receivers in the urban canyons of Hong Kong.
For the experiment conducted in this paper, only the GNSS pseudorange measurement was utilized in the existing FGO-based GNSS/INS integration. The overall potential of the Doppler frequency and carrier-phase measurements has yet to be explored by the authors. To fill this gap, the authors proposed a tightly coupled GNSS/INS integration, using FGO, by exploiting the potential of diverse raw GNSS measurements. The GNSS pseudorange, Doppler frequency, and time-differenced carrier-phase measurements were integrated with the INS, using FGO.
The authors believe the improved performance using FGO-based GNSS/INS integration positioning was due to the global optimization property and the increased measurement redundancy of FGO, compared with the method based on extended Kalman filtering.
Weisong, Hsu; “Factor Graph Optimization for Tightly-Coupled GNSS Pseudorange/Doppler/Carrier Phase/INS Integration: Performance in Urban Canyons of Hong Kong.”
3D mapping in urban environments aided by surround mask GNSS/lidar SLAM
Automatic driving with coupled GNSS/INS and lidar sensors has been implemented in many urban environments successfully over the years. However, this technology is still prone to errors. These potential errors are especially evident in challenging environments, such as urban canyons with several moving objects and building layouts that provide unexpected and abnormal features for lidar sensors and multi-path for GNSS signals.
To address these error challenges in urban environments, the authors of this paper proposed a surround mask that explores error sources from surrounding environments, which could subsequently improve the performance of an integrated mapping system. The surround mask in this experiment extracted a two-layer factor, including non-line-of-sight detection and static objects detection, to collectively compensate for the specific drawbacks of the lidar-based SLAM and the navigation system.
The authors explain that the surround mask eliminated the need to apply complex post-processing to eliminate the accumulated error for each observing unit.
The experimental results demonstrated that the proposed surround mask detected the represented error sources in the local coordinate and provided environment-awareness information for the integrated mapping system.
Ai, Luo, El-Sheimy; “Surround Mask Aiding GNSS/LiDAR SLAM for 3D Mapping in the Dense Urban Environment.”
Novel process noise model helps GNSS Kalman filter degradation in busy cities
Improving the accuracy of GNSS positioning in urban environments is difficult, especially when using low-cost GNSS receivers. In this paper, the authors showed that if the process noise covariance is turned up in a “naïve” manner for poor satellite geometry, the estimation-error covariance could become unintentionally large in a certain direction.
The unintentional inflation of estimation-error covariance could cause the degradation of accuracy. The authors also proposed a fictitious process noise covariance based on an extension of a novel process noise model, which was proposed in their previous work.
The authors stated that in Kalman filter for GNSS positioning, the process noise covariance is often bumped up to avoid the filter divergence in the presence of unknown model errors, by assuming there is a fictitious process noise in addition to the nominal process noise. In this study, the fictitious noise covariance is determined based on the observation matrix, step-by-step, and it reduced the estimation errors without causing the unintentional inflation of estimation-error covariance.
The effectiveness of the derived process noise model is demonstrated for the data sets that simulate GNSS signals from the antenna that moves from open sky areas to urban areas. The estimation errors with the derived process noise model were significantly reduced, compared to the ones with other two process noise models.
Takayama, Yoji, Urakubo, Takateru, Tamaki, Hisashi; “Avoiding GNSS Kalman Filter Degradation in Urban Canyons with a Novel Process Noise Model.”
3D lidar-aided GNSS RTK positioning for increased accuracy mapping in urban canyons
The GNSS real-time kinematic (RTK) positioning technique has shown centimeter-level absolute results in open-sky areas; however, it can suffer from polluted GNSS measurements and poor satellite geometry in urban environments. This is due to the non-line-of-sight (NLOS) and multipath reception caused by signal blockage and reflection.
In this paper, the authors stated that lidar sensors integrated with odometry systems that include an inertial measurement unit (IMU) provided a precise environment description and short-term accurate relative positioning capabilities that could be utilized for aiding GNSS-RTK to obtain better performance.
While 3D lidar-aided GNSS RTK positioning methods detect the GNSS NLOS receptions via an incrementally built map and improve the satellite geometry using the low-lying virtual satellite from lidar features, the high-elevation angle NLOS receptions cannot be fully detected, and the multipath signals cannot be effectively mitigated.
In response to this, the authors proposed a 3D lidar-aided GNSS RTK positioning method with iterated coarse to fine batch optimization by a global 3D NLOS exclusion aided by a point cloud map, which enables the detection of high-elevation angle NLOS receptions. Additionally, the authors proposed iterated batch optimization based on a devised, tightly coupled, factor graph that fully exploited the global consistency among the constraints of lidar, IMU and GNSS RTK to exclude potential multipath signals.
The proposed method aimed to achieve lifelong accurate positioning performance in deeply urbanized areas. The effectiveness of the proposed method has been proved by the evaluation conducted on the author’s open-source challenging dataset, UrbanNav, which contains various sequences collected by automobile-level low-cost GNSS receivers in urban canyons of Hong Kong.
Liu, Wen, Hsu; “3D LiDAR Aided GNSS Real-time Kinematic Positioning via Coarse-to-fine Batch Optimization for High Accuracy Mapping in Dense Urban Canyons.”
Other sources, such as lidar, can be used to aid navigation in the absence of GNSS signals. (Photo: OxTS)
We discussed complementary PNT with Peter Rylands, senior product manager at OxTS.
What are some of the most promising approaches to complementary PNT and how does simulation technology help?
There are two approaches of particular interest. The first is looking at LEO satellite systems that can provide supplementary and potentially more secure methods of navigation, with global coverage from a single system. But these will still suffer from some of the issues GNSS systems experience, namely, what happens when you can’t obtain a signal?
The second is the use of visual aiding through sensor fusion, such as lidar and cameras, that can provide relative positioning (or absolute positioning once you have a space mapped) using SLAM algorithms. While this may increase onboard hardware dependencies, it creates a localized navigation system that can be better protected from malicious actors.
In contrast, closed-loop systems can look to an infrastructure-based system, allowing free movement within the specific area in which the infrastructure is located and a potentially more reliable source of PNT, especially indoors, where GNSS is not available. Ultra-wideband is definitely the up-and-coming technology here, but systems using Wi-Fi, cameras, Bluetooth and others also are being used.
Simulation, as within many domains, allows users to test on a large scale with fewer barriers to entry than real-world testing and an ease in making iterative changes to find an optimal solution. Whether that is to benchmark performance in locations of interest or to change configuration settings to improve visibility or positioning, simulation allows you to do this without the expense of going straight into the environment itself or configuring the actual vehicle under test.
How does OxTS fit in that mix?
OxTS provides customers with the ability to navigate anywhere; whether for reference data in R&D, georeferencing for survey and mapping, or active navigation of autonomous solutions. To do this we provide an IMU-first offering that we then complement with other technologies. Traditionally, this is with GNSS, to form an INS that can provide centimeter-level accuracy. However, we are also aware of the vulnerabilities of GNSS. For us, this is when it becomes an unreliable source of PNT in denied areas, such as indoors, in urban canyons or under tree canopies.
Because of this, we are also investigating and developing complementary solutions that can enhance our offering for users who need confidence in their position even when GNSS is not available. Whether that is through sensor fusion, our Pozyx UWB solution for indoor navigation or other proprietary software and firmware capabilities.
What kinds of complementary PNT are most useful in addressing specifically the challenges posed by jamming and spoofing and how does simulation help?
We need to look at systems that cannot be impacted by, or have mitigations from, the impact of jamming and spoofing. Solutions that are independent of radio communications or satellite use are then valuable in providing this layer of protection. This is where we could look toward OxTS’s use of IMU technology and visual aiding systems. Simulation technologies would then allow you to run hardware-in-the-loop testing, where the primary GNSS solution can have simulated jamming and spoofing to understand the performance of your complementary and protected systems when GNSS cannot be trusted.
Of the 60 exhibitors at the Institute of Navigation’s Joint Navigation Conference (JNC) in San Diego this year, 16 make inertial navigation systems (INS). Many of the other exhibitors integrate INS with GNSS receivers or make simulators to test those integrations. Several exhibitors make a variety of other navigation systems, using active and passive optical sensors, wheel encoders and RF systems that map beacons of opportunity. Only seven manufacturers of GNSS receivers were present.
That’s because the conference — which took place June 6-9 and focused on technical advances in positioning, navigation and timing (PNT) — was hosted by ION’s Military Division for the Departments of Defense (DOD) and Homeland Security. “From an operational perspective,” said the conference program, it focused on “advances in battlefield applications of GPS; critical strengths and weaknesses of field navigation devices; warfighter PNT requirements and solutions; and navigation warfare.” In other words, it was mostly on how to navigate in environments in which the use of GNSS is challenged or denied due to jamming.
The conference program told the story of the GNSS/PNT community’s interests and concerns. Several sessions were on complementary PNT using terrestrial RF signals of opportunity, IMUs, geophysical fields (including gravity and Earth’s magnetic field), celestial objects, ground vision and new commercial sources of space-based PNT, such as satellites in low Earth orbit (LEO).
Other environments in which reliance on GNSS is hard or impossible — such as urban canyons, deep inside buildings, underground and underwater — pose the same navigation challenges to both military and civilian applications. Likewise, jamming is a threat to both. Therefore, several sessions focused on critical infrastructure, demonstrating that the concerns about GNSS vulnerabilities are not just military ones.
Hence the presence among the exhibitors of three manufacturers of atomic clocks, which continue to shrink in size, weight, power and cost (SWaP-C) and are used to assure holdover — that is, the time period required to keep networks synchronized when their primary timing source, usually GNSS, is disrupted or temporarily unavailable. Networks affected include cellphone providers, radio and television broadcasters, financial networks, and the biggest network of all, the Internet.
The JNC “experienced record attendance in both conference participants and exhibitors, hosting more than 1,000 attendees,” Lisa Beaty, ION executive director, told me. She attributed the increase to “the importance of PNT in the nation’s critical infrastructure, current innovation, programmatic funding, and the desire by the DOD community to collaborate and reconvene.” She confidently anticipates additional growth next year.
I am equally confident that much of the cutting-edge technology on display at this conference will find its way into civilian applications in the next few years. Whether in war or in urban canyons, GNSS navigation faces some of the same challenges.
Data shows how successful baseline validation testing of Spirent’s inertial simulation model as compared to real world inertial system performance. Photo: Spirent Federal Systems
We discussed complementary PNT with Roger Hart, head of engineering and Jeff Martin, head of sales at Spirent Federal.
What are some of the most promising approaches to complementary PNT sources and how does simulation technology help?
Roger Hart: The vulnerabilities of GNSS have been recognized. Legacy GNSS are all operating on pretty much the same frequencies and power levels, so, they have some significant common vulnerabilities. There is great interest in finding ways to complement or even replace those capabilities.
Dead reckoning, magnetic and inertial systems have been around for a long time. There are emerging markets to make use of alternative radio frequencies for navigation. In some cases, we are piggybacking on communications signals and deriving PNT from them. In other cases, we are using new PNT signals. A couple that we’ve been focusing on are the alternative navigation systems.
They may be using different orbits, different frequencies, different encoding schemes that set them apart from the legacy GNSS systems, so that, used together, they provide greater resiliency and even stand alone when one or the other system may be affected by interference.
Not to be forgotten is inertial navigation. It’s been around for a long time and is still a standard of navigation. Together with GNSS, it makes it a terrific navigation system. It almost defines complementarity because where GPS is vulnerable inertial can fill in the gaps and where inertial drifts GPS does not. So, paired, they make a very strong system.
At Spirent, we’ve been working with customers to provide a variety of options for both those alternative navigation systems and inertial. Both are a very active field of development and we’re keeping abreast of that.
Jeff Martin: Some good points, Roger. This is something we’ve been engaged in for quite a long time. Since we provide test equipment to the community, it’s critical that we understand what they’re worried about, what the vulnerabilities are. It keeps things exciting, it keeps us on our toes and looking ahead to what’s coming.
What are some of the remaining challenges of integrating GNSS receivers with inertial sensors and, again, how does simulation technology help with that?
Hart: Inertial works by integrating sensor measurements that come in. Therefore, any errors that are present just accumulate over time and can corrupt your navigation solution. So, there’s a strong focus on updating error models and on translating them so that everyday users can use them and get real-life-type performance out of them.
There’s a tendency to think of integrating GPS-INS as putting everything together in one box. There are packages that do that. However, the push now is to go to more distributed systems that are integrated but not packaged in the same box. One example is the all-source positioning and navigation standard that is being developed by the Department of Defense. It will allow you to swap one sensor for another as long as they adhere to the standard. That information all goes back to a sensor fusion engine.
Martin: We have known GNSS simulators well for about four decades. We have been playing in the inertial sandbox for at least a couple of decades as well. This has given us the opportunity to build relationships with the with the key manufacturers and designers of inertial systems. Those relationships have been expanding well beyond inertial to many other sensors and systems that are now coming online. It’s been exciting.
Much work is going into using low Earth orbit satellites for PNT—whether piggybacking on the Iridium satellites or launching new ones. How does simulation help with that?
Hart: It certainly helps with the development of the receivers. The groups that are using these alternative RF and LEO or MEO systems need simulation as they develop the receivers. It gives you the ability to try things certainly before you launch them. At this conference there is considerable interest in making things reprogrammable. We have the NTS-3 satellite, which will be running experiments for different waveforms that can be generated. Even M-code is a step in the direction of giving more flexibility to the signal. It has a lot more flexible cryptography and signal generation than the legacy system with the C/A and P/Y codes.
Our simulation platforms are software based, so we can generate and receive data that can be useful for developing software-defined receivers. It gives you the opportunity to try different waveforms. We have already delivered a satellite-based alternative navigation system simulator. Now, we can build on that one to help the other Leo constellations as they come forward.
Martin: Roger put it well. This is where things get fun. People are concerned with PNT vulnerabilities, so we’re seeing these alternative navigation solutions coming forward. Spirent has done a good job over its nearly 40 years of existence of manufacturing and designing its own hardware and software. It has given us the opportunity to respond quickly. These things are coming fast. People need solutions quickly. We have some solutions already and the platform that we have created gives us the flexibility to develop more. We’re seeing more and more ideas come to fruition and people need to test them. So, this is where it gets fun. We’re excited.
Much work has gone into addressing the enduring challenge of urban canyons. How does simulation technology help?
Hart: Urban canyons are the worst nightmare for GNSS signals. If you’re surrounded by tall buildings, signals are blocked. You may have few or even no satellites in a direct line of sight and many multipath reflections. So, diminished and corrupted signals are available to you. Of course, the more GNSS satellites you have, the better chance you have of getting good signals. But complementing that are radar and vision systems. Those are the ones that will stand out, particularly the vision systems that can read the street signs, see where the curb is, look for parked cars. All those kinds of things will help fill in when you have poor GNSS coverage.
You can observe what’s going on in the environment and simulate it. You can also use our forecasting tool to look ahead.
Martin: This is where things get exciting, isn’t it? In these terrible environments where GNSS is contested—whether it’s an urban environment or one with intentional jamming—there is a lot we can do to help our industry. When this happens in real life, it’s bad news. But when you create that scary situation in the controlled environment of a laboratory, it is great. You can pick things apart and see where you need to improve. I get excited about it. It’s probably the geek in me. It gives us and our partners a lot to look forward to.
How does simulation technology help with sensor fusion?
Hart: It definitely helps you put all the pieces together. You can’t know how your system will work by individually testing each piece. System is the key word here. Simulation enables you to generate the signals and bring them together into a sensor fusion engine. You can test different algorithms. It’s certainly much cheaper and quicker than trying to build this into a product and then test it. Over the decades, simulation has proved itself as a very valuable way in both basic development and integrating the final product.
Martin: That system-wide fusion is where the magic happens.
It sounds like simulation technology—and Spirent Federal in particular—are very much at the center of a lot of the current developments and discussions about complementary PNT. Do you have any final comments?
Hart: As Jeff said, it’s an exciting time. There are many things going on—new technologies, new ways of communicating. It’s a busy time and a bit of a scramble sometimes to keep up with all the new things that are coming.
Martin: People look to Spirent to be their testing resource and it puts us right in the middle of it.
NAVISP includes projects for autonomous and connected driving. (Image: ESA/F. Bagiana)
The European Space Agency (ESA) is issuing a call for ideas to overcome GNSS service gaps in urban canyons by using imaging and 3D mapping technology. A workshop to discuss the call for ideas will be held virtually on July 6.
According to ESA, the growing availability of high-quality image sensors and high-fidelity 3D maps — such as those within smartphone mapping apps — offer a promising way to shrink the performance gap caused by urban canyons and multipath for future mobility applications in terms of ubiquity, reliability and resilience.
NAVISP — ESA’s Navigation Innovation and Support Programme — specifically is seeking prospects for technology demonstrations of mobility tech to support applications such as road, maritime transport and drones. The tech would provide assisted satnav by harnessing image sensors and 3D urban models. The proof-of-concept demonstration projects or national testbeds would facilitate introduction of this technology into commercial products.
Use cases include private or public autonomous transportation in cities, including cars, trams, scooters, bikes, urban ferries, harbors, narrow waterway navigation and future passenger drones.
Reflected satellite navigation signals (multipath) can degrade positioning performance, especially in urban canyons with numerous artificial surfaces. (Image: EUSPA)
The NAVISP project, called a “thematic window,” is titled “Assisted GNSS with Imaging Sensors and 3-D models for Mobility Applications.” The thematic window opened on June 10 and will close on Oct. 31. During its duration, ESA is offering dedicated support to companies interested in participating in the projects and submitting outline proposals.
On July 6, the agency is hosting an online workshop with stakeholders to raise awareness about the initiative and clarify any issues interested companies may have. ESA will present the requirements of the Thematic Window and the application process. The workshop will include presentations from high-level experts covering market perspectives, techniques involved in the use of 3D models and imaging sensors, the state of the art of these technologies and latest advances in visual navigation and artificial intelligence applied to mobility applications.
To register for the July 6 workshop, click here. The workshop agenda is available here.
Swift Navigation‘s new Precision GNSS Module (PGM) is now available. The PGM module is designed to offer fast evaluation and a quick path to production for those requiring a precise positioning solution.
The PGM is available in a simple-to-use, industry-standard mPCIe (mini peripheral component interconnect express) format and is designed specifically for Swift’s Starling positioning engine running on a host application processor to deliver real-time precision navigation.
The PGM utilizes STMicroelectronics’ TeseoV chipset in Quectel’s multi-constellation, dual-band LG69T-AP receiver to create an affordable, easy-to-use solution for customers building industrial, last-mile and internet of things (IoT) platforms, Swift Navigation said.
The LG69T family of products, based on the ST TeseoV, is an designed for demanding precision applications that require centimeter accuracies. The LG69T-AP — supporting L1/L5 bands — has an integrated ST inertial measurement unit and processor to support dead reckoning for signal-compromised areas such as urban canyons, parking lots and underground structures.
According to Swift Navigation, this proven solution is ready for fast and easy integration and deployment — using industry-standard protocols — to reduce customer engineering investment and enable quick time to market.
This solution operates with the highest accuracy when used with Swift’s Skylark cloud-based, wide-area precise positioning service. Skylark delivers accuracy down to 10 cm. The solution supports standard RTCM OSR (Observation Space Representation) and SSR (State Space Representation) correction formats.
Skylark is available for integration into wide-area, high-precision positioning applications across the continental United States and Europe and is available in Japan, South Korea and Australia, with plans underway to expand globally. Skylark is an ever-expanding service and is scalable to service millions of users.
“We are excited to be offering the PGM utilizing the Quectel LG69T-AP receiver,” said Dave Huntingford, staff product manager at Swift Navigation. “The ability to provide a cost-effective, easily integrated solution, complete with corrections, opens up a host of opportunities for IoT, last-mile and industrial customers to benefit from precise positioning.”
“Quectel is delighted to be working with Swift Navigation to provide the market with an easy-to-use precision GNSS solution,” said Mark Murray, vice president of sales for GNSS and automotive at Quectel Wireless Solutions. “The LG69T-AP, together with Swift’s Starling positioning engine and Skylark corrections, is perfect for supporting applications and markets where <10-cm accuracy is required.”
This product is available today with full production by the first quarter of 2021; an evaluation kit is available. Contact Swift Navigation or Quectel.
Quectel Wireless Solutions Co. Ltd., in association with STMicroelectronics, has released the LG69T module, an automotive-grade dual-band high-precision GNSS module that integrates dead-reckoning (DR) and real-time kinematic (RTK) technologies.
The new Quectel module, announced at 2019 Apsara Conference in Hangzhou, is designed to facilitate open-sky positioning performance with an accuracy of up to 10 centimeters, which is currently the industry’s most advanced positioning technology for the automotive market. LG69T will support next-generation precision positioning capabilities for smart vehicles and autonomous driving scenarios.
The Quectel LG69T GNSS module is based on ST’s STA8100GA, the latest Automotive-grade dual frequency positioning chip with 80 tracking channels and four rapid-acquisition channels that are compatible with many constellations: GPS, BeiDou, Galileo, NAVIC/IRNSS and QZSS.
It is an AEC-Q100-qualified dual-band (L1 + L5) GNSS module that integrates multi-band RTK technology for centimeter-level accuracy.
The LG69T module’s dead-reckoning capabilities feature an integrated inertial measurement unit (IMU) that provides continuous high-precision positioning. The LG69T supports corrections input for standard Radio Technical Commission for Maritime Services (RTCM) and centimeter-level navigation by using RTCM data from a third — local base stations. The module performs well under the highly challenging conditions of urban canyon environments.
“We are thrilled to collaborate with STMicroelectronics on our newest generation of high-precision positioning module,” said Min Wang, Quectel’s automotive product line general manager. “With this highly-integrated LG69T module, automakers and Tier 1 suppliers will no longer have to spend time selecting components, integrating hardware, adapting interfaces and conducting tests and verifications, which will greatly cut their time-to-market and costs, and help them accelerate the deployment of autonomous driving to seize early opportunities.”
“ST has strong experience and is the Global Automotive High Precise Positioning Technology and Market Leader. We are very proud to cooperate with leading Chinese smart driving high technology company,” said MH TEY, Greater China, South Asia and Korea automotive marketing and application head of department, STMicroelectronics. “Today, there is growing dependency on high-performance GNSS in automotive applications such as navigation, safety and autonomous driving. With this cooperation, we are very confident to become the market leader by providing cost-effective and unique best-in-class solution for autonomous vehicle.”
Engineering samples of Quectel’s LG69T module will be offered to automakers and Tier 1 suppliers by the end of 2019, and the product will be commercially available around mid-2020 and is expected to be deployed in mass produced models as early as 2021.
How will wireless technologies most significantly drive change and innovation in the surveying industry?
Miguel Amor
“GNSS by design, by physics, will always be challenged in urban settings. 5G and GNSS will provide a step to ubiquitous positioning in built-up areas — a blend of relative and absolute positioning, terrestrial and satellite-based measurements.” Miguel Amor Hexagon Positioning Intelligence
Greg Turetzky
“The improvements in bandwidth and latency of 5G will create new opportunities for edge and cloud-based computing advances such as AI and machine learning to penetrate surveying, as 5G is doing in other industries, to improve efficiency, accuracy and automation.” Greg Turetzky Consultant
Members of the EAB
Tony Agresta Nearmap
Miguel Amor Hexagon Positioning Intelligence
Thibault Bonnevie SBG Systems
Alison Brown NAVSYS Corporation
Ismael Colomina GeoNumerics
Clem Driscoll C.J. Driscoll & Associates
John Fischer Orolia
Ellen Hall Spirent Federal Systems
Jules McNeff Overlook Systems Technologies, Inc.
Terry Moore University of Nottingham
Bradford W. Parkinson Stanford Center for Position, Navigation and Time
Furuno Electric Co. Ltd., based in Nishinomiya, Japan, has developed the GT-88 timing module and GF-8801/02/03/04/05 disciplined oscillator for users who require UTC time-synchronized signals to meet the new 5G requirements.
They provide UTC time-synchronized timing signals (1 PPS/10 MHz) by receiving GNSS satellite signals. Achieved stability is better than that of an atomic clock, including a rubidium.
Photo: Furuno
The GT/GF-88 series includes a brand-new algorithm, named Dynamic Satellite Selection, that provides outstanding multipath mitigation, especially in urban canyon environments, the company said. The algorithm was developed by Nippon Telegraph and Telephone Corporation (NTT) based in Tokyo, Japan.
Extremely high stability of 4.5 ns (1 sigma) is obtained, only requiring reception of the L1 band (1575.42 MHz) frequency GNSS satellites. It was achieved by improving advanced position estimation algorithms and optimizing position calculation among several different GNSS satellite constellations. It allows users to achieve 5G-required performance without any changes to existing single-band GNSS antennas.
It incorporates the Dynamic Satellite Selection, an advanced multipath mitigation algorithm developed by NTT. Normally typical time synchronization performance deteriorates in urban canyon environments by the effect of multipath. The Dynamic Satellite Selection reduces this time error by one-fifth. This provides more flexibility when installing GNSS antennas. Consequently, the GT/GF-88 series now permits GNSS antennas to be mounted on walls, windows of tall buildings and other difficult reception environments.
The GT/GF-88 series continues to support GPS, GLONASS and QZSS satellite constellations, and now adds Galileo support. As the total number of satellites available increases, operational stability also increases.
Honeywell has produced a new inertial navigation unit that provides accurate navigation for customers across a broad range of industries including agriculture, robotics and autonomous vehicles, without compromising on size, cost or performance.
The HGuideN580 inertial navigation technology improves accuracy in urban and rural environments. (Photo: Honeywell)
The HGuide n580 is the first Honeywell-produced, industrial-focused navigation solution that uses both precision inertial measurement unit technology and GNSS to improve location accuracy even when facing natural and manmade obstacles.
“The blend of inertial and satellite navigation capabilities provided by the HGuide n580 is especially important where precision is required in demanding environments — for example, autonomous cars traveling in cities, where our technology can extend the accuracy and performance of navigational systems while keeping passengers safe,” said Chris Lund, senior director, Navigation and Sensors, Honeywell Aerospace. “Honeywell’s history and expertise in navigation technology enables customers to implement this new wave of advanced technology into their own applications and operations.”
Roughly the size of a deck of cards, the HGuide n580 gives Honeywell’s industrial customers the capabilities needed to navigate accurately in areas with limited satellite coverage, such as densely populated cities where tall buildings, underground tunnels, and multi-layer freeway stacks or bridges often create challenges to traditional GPS navigation.
For a GPS unit to function properly, it requires a strong signal connection between the unit on the ground and multiple satellites in the sky to accurately orient its position. City infrastructure such as buildings and tunnels can temporarily block the signal between GPS unit receivers and satellites, creating urban canyons.
With the HGuide n580 integrated system, Honeywell’s inertial measurement unit technology combines with GPS to act as a backup solution, which means the loss of GPS signal caused by an urban canyon does not result in a complete loss of navigation.
To learn more about the new HGuide n580 solution and Honeywell’s other commercially available navigation technologies, visit the Honeywell Aerospace website.
SINGLE-FREQUENCY GPS POSITIONING. Can it get any better? In the March 2018 edition of this column, we looked at the development of precise point positioning or PPP — the (mostly) carrier-phase-based positioning technique using satellite orbit and clock data significantly more precise than that available in the broadcast navigation messages. We noted that dual-frequency PPP can achieve horizontal positioning accuracies better than 10 centimeters. On the other hand, single-frequency pseudorange-based GPS positioning using broadcast data (by far, the most common use of GPS) provides meter-level accuracy at best. And “at best” means under ideal conditions with no sky obstructions, negligible multipath, a benign ionosphere and healthy signals.
But what about the more typical conditions experienced while navigating in urban environments such as blocked signals and reception of reflected or non-line-of-sight signals and multipath-contaminated signals? And what if the ionosphere is disturbed to boot? A standard unaugmented single-frequency GPS receiver will be lucky to get consistent accuracies much below 10 meters. In some cases, positioning accuracy is compromised by the relatively inexpensive antenna and receiver hardware used in devices for the mass consumer market. That includes the positioning units in smartphones and vehicle satnav units. True, 10-meter accuracy positioning might be quite acceptable for certain applications including basic navigation to get from point A to point B. But there are many situations that we encounter in our daily lives where a predictable accuracy of 1 meter or better could be hugely useful such as identifying the correct lane in which a vehicle is traveling or identifying a particular parking space — not to mention various vehicle-to-vehicle positioning and situational awareness needs.
Sure, we can augment a GPS receiver with other devices such as inertial sensors, barometers, wheel-speed sensors and the like. And they can, indeed, be a big help. But can we improve the capability of the standalone GPS receiver?
For a long time, the use of multiple-constellation receivers has been touted as a panacea for blocked signals in cities. Since the 1990s, we have had two working satellite constellations: GPS and GLONASS. Yes, GLONASS has had its up and downs, but it has provided a more or less full constellation for a number of years now, and many consumer-level devices include a GLONASS capability nowadays. Some of the latest devices also sport the ability to use signals from the European Galileo and Chinese BeiDou systems now nearing completion.
While one might still have large dilutions of precision using a multi-constellation GNSS receiver, in general, even one additional satellite signal can be beneficial in improving accuracy or navigation continuity. Receiver chips with the ability to provide useful carrier-phase measurements will also be hugely beneficial, and we are already seeing developments in this regard in the smartphone market.
We should also mention that there can be significant differences in the performance of different kinds of antennas and their effect on positioning capabilities in the same environment. And, of course, how the measurements from different satellites are combined in a receiver’s processor can have an effect on the resulting position accuracy.
In this month’s column, I am joined by one of my graduate students, Ivan Smolyakov, who has carried out some real-world tests with the aim of improving single-frequency GNSS positioning in urban environments. The initial tests (using a survey-grade receiver to be replaced with more modest equipment in subsequent testing) concentrated on the benefit of using GLONASS alongside GPS, the effect of different antennas, and adaptive weighting of observations. Single-frequency accuracies below one meter? You bet.
A new generation of mobile platforms equipped with chips allows continuous carrier-phase tracking, lifting applications based on localization to the next level. Whether in transportation, pedestrian navigation or safety-of-life services, a robust position determination is required in various environments including cities.
Navigation in urban environments is significantly challenged by signal degradation. Typical urban scenarios result in blocked signals, reception of non-line-of-sight (NLOS) signals and multipath-contaminated signals. Low-cost single-frequency equipment suffers the most from such effects as a consequence of hardware limitations, while also being affected by potentially poor satellite geometry.
This article addresses the challenge for mobile platforms equipped with low-cost single-frequency receivers and patch antennas to efficiently utilize all GNSS signals available.
Various techniques attempt to minimize the impact of NLOS and multipath on a final solution: weighting based on the elevation angle of a satellite and signal-to-noise ratio of its signal, as well as exclusion of certain satellites from processing, selecting the most consistent set of satellites. In our work, we explored this approach, combining the aforementioned methods with automatic stochastic model adjustment. Signal degradation demonstration and algorithm testing was performed on 1-Hz combined GPS and GLONASS static and kinematic datasets collected in an urban environment. Our proposed algorithm yielded sub-meter-level positioning accuracy and showed a 10 percent accuracy improvement compared to regular weighting and satellite-exclusion-based algorithms.
In the past several years, the number of applications that at least to some extent depend on GNSS has increased dramatically. Precise point positioning (PPP) solutions propagated to common everyday uses and started to lead the way as a key method for coordinate determination in the low-cost regime of navigation. This area could be characterized by the necessity of real-time coordinate determination with a sub-meter/decimeter accuracy requirement and often with the expectation of reaching that level of accuracy in the most challenging environment for satellite navigation: the urban setting.
Tall buildings, tree foliage and the presence of reflective surfaces decrease the number of available satellites and result in reception of NLOS signals, as well as in reception of signals contaminated by multipath. The field of aided navigation addresses the problem by using additional devices and external information along with GNSS, such as tightly coupled inertial sensors or 3D mapping of the surrounding environment. Another way to deal with these degrading effects is to address their existence directly by means of consistency checking and outlier mitigation. However, while being effective, these types of algorithms can often create an excessive computational load, which limits their use for low-cost applications.
On the GNSS side, the problem also could be addressed by detecting faulty signals and adapting filtering parameters accordingly, making sure that incorrect a priori statistical information is not used as it can lead to solution degradation. Many adaptive techniques were developed, reducing the need to accurately know a priori filtering parameters.
Our research attempts to maximize the use of pure GNSS in the context of standalone low-cost single-frequency positioning, adjusting filter parameters in a way consistent with the surrounding environment. First, the vulnerability of low-cost patch antennas towards NLOS and multipath-contaminated signals has been investigated through a comparison to higher quality antennas in an observation campaign carried out in an urban environment. Second, based on preliminary analysis of findings and inspired by past work, we developed an adaptive weight adjustment algorithm with minimal computational load, aiming to address a rapidly changing surrounding multipath environment. The proposed algorithm was tested in GPS-only and combined GPS + GLONASS static and kinematic scenarios.
OBSERVATION CAMPAIGN
The idea behind the observation campaign was to highlight unwanted low-cost patch antenna vulnerability to multipath and NLOS signals. Three antennas were mounted on the roof of a car (see FIGURE 1): a high-grade antenna (Leica AX1203+ GNSS with 29 dB low-noise-amplifier (LNA) gain), a consumer-level patch antenna priced around $150 (Tallysman TW3470 with 40 dB LNA gain) and a truly low-cost patch antenna (Chang Hong Information Co., GPS Active 28 dB Magnetic Antenna) priced around $10.
FIGURE 1. Experimental setup. Tested antennas from left to right: Tallysman TW3470, Leica AX1203+ GNSS, low-cost patch antenna (Chang Hong Information Co.).
Paired with each antenna, we used geodetic quality receivers of the same model (Javad Triumph-LS) with identical configurations, which yielded the best possible performance on the receiver side, meaning that differences in analyzed behavior are mostly dependent on the antenna type. After the start of observations, the experimental setup remained stationary for 30 minutes in a parking lot environment, followed by an approximately 30-minute drive through downtown Fredericton, New Brunswick.
Road situations encountered included passing under a bridge and a traffic jam caused by road construction. These circumstances introduced complete signal blockage, as well as multipath-contaminated and NLOS signal reception. The Javad receivers recorded observables at a 5-Hz rate. We subsequently decimated the data to 1 Hz for post-processing. The GPS and GLONASS L1 pseudorange and carrier-phase observations (C1C and L1C in RINEX terminology) were used for the single-frequency positioning solutions.
METHODOLOGY
The results shown in this article were obtained using post-processing. However, the described technique is ready for implementation in real time. The undifferenced measurements model was selected as an approach commonly adopted for truly low-cost positioning platforms. Multipath is notoriously difficult to reliably estimate in a filter. Instead, our proposed technique takes advantage of the pseudo-multipath (also referred to as “code-minus-phase”) observable and a statistical analysis applied to its time series.
Observation Model. Given that the target equipment is low cost, the complexity of the observation model should be taken into account. The observables were modeled as follows:
ρ is the geometric range between antenna phase centers of receiver and satellite (m),
c is the speed of light in vacuum (m/s),
dT is the receiver clock offset (s),
dt is the satellite clock offset (s),
T is the tropospheric delay (m),
I is the ionospheric delay (m),
λ is the wavelength of the carrier (m),
N is the carrier-phase ambiguity
M, m is the multipath effect on pseudorange and carrier-phase measurements, respectively (m),
ϵP, ϵΦ is the measurement noise and any residual bias for pseudorange and carrier-phase measurements, respectively, including the effect of any dynamics-induced tracking loop errors (m), and
j represents a particular satellite.
The majority of modern mobile platforms have Internet access, and in this research it was assumed that information on satellite orbits, clock offsets and ionospheric delays could be acquired through real-time precise correction streams. For our computations, we used orbits and clocks from the Centre National d’Etudes Spatiales as well as ionospheric delays derived from European Space Agency global ionospheric maps (GIMs). The range term was corrected for Earth tides, ocean loading and relativistic effects.
In our study, coordinate determination is handled with a standard implementation of Kalman filtering. The Kalman filter state vector contains receiver coordinates, receiver clock, carrier-phase ambiguities and tropospheric delay.
Automatic Weight Adjustment. Our study revisited the technique developed by Bisnath and Langley (see Further Reading). First, the pseudo-multipath observable is calculated:
The term 2Ij in Equation (3) can be partially eliminated by applying a GIM correction. The pseudo-multipath observable gives a good representation of code multipath, as the magnitude of the carrier-phase terms in Equation (3) is much smaller than the corresponding pseudorange terms.
Pseudo-multipath observables are stored in a buffer of a size B1 and are used to calculate sample variances for each satellite (see FIGURE 2). When B2 variances are stored in a second buffer, the algorithm has enough data to make a decision as to whether the weights of the observables should be adjusted. The challenging part of the algorithm is the threshold determination, which will be discussed in subsequent sections.
FIGURE 2. Block diagram of the environment detection and weight adjustment algorithm.
TESTING AND RESULTS
We collected an urban dataset consisting of two segments: one stationary and one kinematic. The stationary segment was inspected since in this case the multipath patterns are not randomized by the moving surroundings as in the kinematic segment. When the weighting scheme was developed, we proceeded with its tuning and analyzed its performance in the more challenging, kinematic environment and also added GLONASS observations to the processing.
Preliminary Analysis. First, the behavior of the pseudo-multipath observable during the observation session was analyzed. The initial processing was carried out in GPS-only mode, applying an elevation-angle weighting scheme and 10-degree elevation mask angle. The reference coordinates were obtained with the PPP software developed at UNB using Leica AX1203+ GNSS dual-frequency observations. Thirty-minute static datasets showed that the horizontal error of the coordinates determined with patch antenna observations is just below the 2-meter mark, while the 3D-error is above 5 meters with height error being the biggest contributor (see FIGURE 3).
FIGURE 3. Absolute errors for GPS-only processing, 30-minute static session. Comparison among antennas.
The errors of higher grade antenna datasets proved to be significantly smaller with all error components being below the 0.5-meter mark. The comparison presented in FIGURES 4 and 5 shows a more perturbed behavior of the pseudo-multipath observable in the case of the low-cost patch antenna compared to the Tallysman (static and kinematic parts of the session are presented in the same plot). Interestingly, this behavior is not common for all the satellites tracked; only two of them (G12 and G09) show a high variation in the pseudo-multipath observable and only for periods of time with stable periods in between.
FIGURE 6 illustrates the pseudo-multipath observable compared among three antennas for satellite G12. It shows that, as might be expected, higher grade antennas perform better in terms of multipath rejection. Both G12 and G09 were more than 30 degrees above the horizon and normally would not be excluded from processing. The attempt of applying a weighting scheme based on the carrier-to-noise-density ratio C/N0 did not introduce any accuracy improvement. Indeed, C/N0 values did not show any visible correlation with the illustrated multipath contamination.
FIGURE 6. Pseudo-multipath observables comparison for GPS satellite G12.
We empirically determined that the optimal size of buffer B1 for the 1-Hz low-cost patch antenna data is close to 20 epochs. This value allows the algorithm to trigger adequate increases of variances when the pseudo-multipath observable is perturbed and keep all “good” signals below the calculated threshold. The threshold is determined by statistical analysis of buffer B2 of a reference satellite (see Figure 2).
FIGURE 2. Block diagram of the environment detection and weight adjustment algorithm.
We found it to be a good practice to select the reference satellite as one above 70 degrees elevation angle and with minimal sample variance for low-cost antenna data processing. FIGURE 7 shows the variance behavior for three GPS satellites: calculated statistics allow the algorithm to trigger the adaptive weighting algorithm for multipath-contaminated signals of satellite G12, while G02 and G03 follow the normal elevation-angle-dependent weighting scheme.
FIGURE 7. Pseudo-multipath sample variance comparison among three satellites for the static part of the campaign. Low-cost patch antenna observations.
Static Session. In GPS-only mode, applying the proposed algorithm allowed for a decrease in positioning absolute error for the low-cost patch antenna of more than 50 percent. Horizontal error was brought down to the sub-meter level, while vertical error remained the biggest error contributor being just above 2 meters (see FIGURE 8).
FIGURE 8. Absolute errors for positioning with low-cost patch antenna, 30-minute static session; processing methods comparison.
A comparison of convergence behaviors among the tested antennas and methods for the stationary setup in GPS-only mode indicated the convergence behavior dependency on the applied multipath-rejection efforts. Higher grade antennas capable of reducing multipath to some degree demonstrate much more stable convergence to reference coordinates, while the adaptive weighting algorithm partially eliminates the residual multipath effect at the software level.
As was shown by Lou et al., for example (see Further Reading), single-frequency positioning solutions can benefit from the integration of additional satellite constellations. Here, we report on testing a combined GPS+GLONASS model. For the static case, combined processing outperformed the GPS-only model with adaptive weighting by almost 1 meter in 3D error and improved height estimation by more than 50 percent. The weight adaptation algorithm introduced only a slight improvement in combined processing (see Figure 8).
Kinematic Session. Kinematic standalone positioning is especially challenging in the case of low-cost equipment utilization. The surrounding environment is constantly changing, which is illustrated by a shift in the behavior of the pseudo-multipath observables (see Figures 5 and 6), the C/N0, and the satellite availability.
The reference trajectory for kinematic testing was computed with the Leica AX1203+ GNSS antenna and receiver combination using dual-frequency data with the PPP software developed at UNB. When compared with the reference trajectory, the standard GPS-only solution experiences jumps as large as 9 meters in the horizontal plane and 15 meters in height. Application of the adaptive weighting technique to the same dataset noticeably improves the solution, decreasing the size of jumps in all coordinates (see FIGURE 9).
FIGURE 9. Low-cost patch antenna GPS-only solution superimposed on a georeferenced Google Map: no adaptation (red) and with adaptive weighting (green).
Understandably, the most efficient approach is the additional constellation integration. We estimate that 70 percent of the trajectory was determined with sub-meter horizontal accuracy when the GPS+GLONASS model was used. The adaptive weighting technique showed only minor improvements when applied to the combined model, which brings us to the conclusion that the stochastic model in the proposed algorithm needs to be investigated further.
CONCLUSIONS
Our research experiment allowed us to monitor the performance of low-cost versus high-grade GNSS antennas. The pseudo-multipath observable was shown to be an effective measure to trace the impact of multipath on a navigation signal. Analysis of subsequently calculated variances allowed our algorithm to automatically assess multipath environments and implement an adaptive weighting technique.
The technique proved to be especially effective for use with low-cost patch antenna observations in a GPS-only mode, providing a more than 50 percent increase in accuracy in a static case and noticeable compensations in coordinate jumps in kinematic mode. We intend to further improve the algorithm to potentially make a bigger impact on the combined GPS+GLONASS solution. The automatic adjustment of filtering parameters such as process noise in the Kalman filter can be considered for future research.
ACKNOWLEDGMENTS
Our research is supported by the Natural Sciences and Engineering Research Council of Canada. The authors thank Ryan White at the University of New Brunswick (UNB) for assistance with the observation campaign and Marco Mendonça, also at UNB, for helpful feedback on our work along the way. This article is based on the paper “Adaptive Algorithm for Low-cost Single-frequency Positioning in Urban Environments: Design and Performance Analysis” presented at ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018.
Ivan Smolyakov is a Ph.D. student in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick (UNB) under the supervision of Richard B. Langley. His research efforts are concentrated on single-frequency precise point positioning challenges.
Richard B. Langley is a professor in the Department of Geodesy and Geomatics Engineering at UNB, where he has been teaching and conducting research since 1981. He has a B.Sc. in applied physics from the University of Waterloo and a Ph.D. in experimental space science from York University, Toronto. Langley has been active in the development of GNSS error models since the early 1980s and has been a contributing editor and columnist for GPS World magazine since its inception in 1990. He is a fellow of The Institute of Navigation (ION), the Royal Institute of Navigation and the International Association of Geodesy. He was a co-recipient of the ION Burka Award for 2003 and received the ION Johannes Kepler Award in 2007.
FURTHER READING
• GPS and Multi-GNSS Single Receiver Positioning
“Multi-GNSS Precise Point Positioning with Raw Single-frequency and Dual-frequency Measurement Models” by Y. Lou, F. Zheng, S. Gu, C. Wang, H. Guo and Y. Feng in GPS Solutions, Vol. 20, No. 4, October 2016, pp. 849–862, doi: 10.1007/s10291-015-0495-8.
“Intelligent Urban Positioning using Multi-Constellation GNSS with 3D Mapping and NLOS Signal Detection” by P.D. Groves, Z. Jiang, L. Wang and M.K. Ziebart in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 17–21, 2012, pp. 458–472.
“Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons” by L.-T. Hsu, H. Tokura, N. Kubo, Y. Gu and S. Kamijo in IEEE Sensors Journal, Vol. 17, No. 6, March 15, 2017, pp. 1909–1917, doi: 10.1109/JSEN.2017.2654359.
“Robust Outlier Mitigation in Multi-Constellation GNSS Positioning for Waterborne Applications” by J.A. Pozo-Pérez, D. Medina, I. Herrera-Pinzón, A. Heßelbarth and R. Ziebold in Proceedings of ION ITM 2017, the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, Jan. 30 – Feb. 2, 2017, pp. 1330–1343.
“Least-Squares Estimation and Kalman Filtering” by S. Verhagen and P.J.G. Teunissen, Chapter 22 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
Australian company Cohda Wireless has released a vehicle positioning system to eliminate GPS black spots in “urban canyons” between high-rise buildings.
Using Cohda’s expertise in developing collision avoidance systems for mines, the vehicle-based system, V2X-Locate, can identify vehicle position to sub-meter accuracy in environments that degrade GPS accuracy, such as tunnels, underground carparks and between high-rise buildings.
As well as enhancing current connected vehicles, V2X-Locate delivers a critical component for connected autonomous vehicles (CAV), which will require uninterrupted positioning data to safely navigate on roads, the company said.
Image: Cohda Wireless
Cohda has designed V2X-Locate to enable equipped vehicles to identify their location using existing Smart City V2X (vehicle-to-everything) roadside infrastructure from any standards-based manufacturer.
Cohda Wireless Chief Technology Officer Paul Alexander said V2X-Locate was a globally unique product. “We solve the problem caused by GPS and satellite-based positioning systems not working in all use-cases,” he said.
“If you’re in a major downtown area with tall buildings, or in a tunnel or in an underground parking lot, a GPS system can fail, preventing it from delivering accurate results,” Alexander said. “As well as being inconvenient for current drivers, this is not an option as we enter the era of driverless cars. The V2X-Locate breakthrough is to position the vehicle with sub-meter accuracy by using the existing communications signals produced by V2X Smart City infrastructure deployments. The result is that V2X-Locate can eliminate positioning black spots in city centers where they are most likely to occur.”
Cohda Wireless demonstrated V2X-Locate in a 2017 trial in New York City, where it repeatedly demonstrated sub-meter accuracy while driving along Sixth Avenue, which has the tallest buildings in the Big Apple. Comparably tested GPS-based systems were as much as tens of meters off-course, at times showing cars driving through buildings.
Alexander said Cohda Wireless had designed V2X-Locate by using its experience developing collision avoidance technology for underground mines. “The hardest place to do positioning is one kilometer underground with a cubic kilometer of copper above your head,” he said.
“That’s where V2X-Locate was born,” Alexander said. “Cohda has worked in that area for several years, providing accurate positioning for vehicles where no GPS connectivity is available. We’ve now successfully migrated that technology from mine sites of the outback to the urban canyons of New York City.”
V2X_Locate uses the NXP SAF5400 single-chip modem for V2X. (Photo: NXP)
Both Cohda’s standard V2X onboard units and roadside units use the NXP RoadLINK chipset, which supports V2X-Locate’s highly accurate performance by delivering multipath channel tracking.
After a pre-release international roadshow in October last year, Cohda Wireless received strong interest in V2X-Locate from both Smart Cities and Tier 1 automotive manufacturers. To meet that demand, Cohda Wireless has released a V2X-Locate Evaluation Kit, which contains the system and four roadside unit devices, which equip prospective customers to put V2X-Locate through its paces.
