UCL Geomatics

Robust Positioning and Navigation

Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), have revolutionised positioning and navigation over the past 20 years. However, much more work is needed to achieve reliable metres-level positioning everywhere all of the time. GNSS performance is still poor in challenging environments such as dense urban areas and indoors, while jamming and interference is now a threat to many civilian applications. Robust positioning across all environments requires a multi-sensor approach, augmenting multi-constellation GNSS with other signals, motion sensors, and environmental features. This may be enhanced using 3D mapping, context awareness and cooperation between users. Robustness is maximised by harvesting as much information from the environment as possible and then selecting the most reliable measurements for determining the position.

Robust Positioning and Navigation is a program of research led by Dr Paul Groves. It is one of three research programs within UCL Engineering’s Space Geodesy and Navigation Laboratory (SGNL).

Vision

Our vision is a world where a real-time low-cost position solution within three metres is available in all places at all times. This includes challenging environments such as dense urban areas, indoors, underground and underwater. It also includes times when some technologies may become unusable due to signal interference, weather disturbances, unusual dynamics or equipment failure. To achieve this vision, a combination of different navigation and positioning technologies, both conventional and new, must be used.

Mission

  • To devise, develop and demonstrate new methods of navigation and positioning, and novel ways of improving existing techniques.
  • To advance the rise of a new generation of multisensor integrated navigation systems by embracing the challenges of complexity, context and ambiguity.
  • To inspire innovation in the navigation and positioning community through the promotion of knowledge.
  • To make sure better positioning benefits society and the economy.

Current and Recent Research

Details of the following projects and activities, including links to publications, are presented below.

  • Novel GNSS multipath and NLOS reception mitigation techniques for urban areas
  • GNSS shadow matching: A new positioning techniques for urban canyons using 3D city modelling
  • Pedestrian Motion Analysis and Modelling in Support of Indoor and Outdoor Positioning
  • Better navigation performance using ultra-low-cost inertial sensors
  • Positioning using location signatures from multiple environment sensors
  • Better mobile positioning using advanced 3D mapping to enhance satellite navigation
  • Intelligent Positioning for Cities
  • Context detection, categorization and connectivity for advanced adaptive integrated navigation
  • Future integration architectures for complex multi-sensor navigation and positioning systems
  • Differential positioning by modulation correlation using signals of opportunity

Novel GNSS multipath and NLOS reception mitigation techniques for urban areas

Non-line-of-sight (NLOS) reception is the major cause of large GNSS position errors in dense urban environments. Multipath interference is also a severe problem. With a multi-constellation GNSS receiver there is scope to select the best signals for positioning and reject the rest. However, the challenge is to identify which signals are contaminated by NLOS reception or severe multipath errors and which are not. Under this project, a new NLOS detection technique using a dual-polarisation antenna has been developed and demonstrated. A new consistency-checking technique based on subset comparison has been developed and shown to perform better than the conventional sequential elimination approach. A new multipath detection technique using three-frequency carrier-power-to-noise-density ratio measurements has also been developed. Most of this activity formed part of the the Innovative Navigation using new GNSS Signals with Hybridised Technologies (INSIGHT) collaborative research program.

Project duration: 2010-2013; Sponsor: EPSRC; Researchers: Dr Ziyi Jiang.
Visiting Researcher (2012-2013): Li-Ta Hsu

Groves, P. D., Jiang, Z. Y., Skelton, B., Cross, P. A., Lau, L., Adane, Y., & Kale, I. (2010). Novel Multipath Mitigation Methods using a Dual-polarization Antenna. Proceedings of ION GNSS 2010

Jiang, Z., Groves, P. D., Ochieng, W. Y., Feng, S., Milner, C. D., & Mattos, P. G. (2011). Multi-constellation GNSS multipath mitigation using consistency checking. Proceedings of ION GNSS 2011, 3889-3902.

Jiang, Z., & Groves, P. D. (2012). GNSS NLOS and Multipath Error Mitigation using Advanced Multi-Constellation Consistency Checking with Height Aiding. Proceedings of ION GNSS 2012.

Jiang, Z., & Groves, P. D. (2012). NLOS GPS signal detection using a dual-polarisation antenna. GPS Solutions, 1-12. doi:10.1007/s10291-012-0305-5

Hsu, L. T., Groves, P. D., & Jan, S. S. (2013). Assessment of the Multipath Mitigation Effect of Vector Tracking in an Urban Environment. Proceedings of ION Pacific PNT 2013.

Groves, P. D., & Jiang, Z. (2013). Height Aiding, C/N 0 Weighting and Consistency Checking for GNSS NLOS and Multipath Mitigation in Urban Areas. Journal of Navigation, 1-17. doi:10.1017/S0373463313000350

Groves, P. D., Jiang, Z., Rudi, M., & Strode, P. (2013). A Portfolio Approach to NLOS and Multipath Mitigation in Dense Urban Areas. Proceedings of ION GNSS+ 2013.

Groves, P. D. (2013). GNSS Solutions: Multipath vs. NLOS signals. How Does Non-Line-of-Sight Reception Differ From Multipath Interference. Inside GNSS Magazine, 8 (6), 40-42, 63.

Hsu, L. T., Jan, S. S., Groves, P. D., & Kubo, N. (2014). Multipath mitigation and NLOS detection using vector tracking in urban environments. GPS Solutions. doi:10.1007/s10291-014-0384-6.

Strode, P. R. R; & Groves, P. D. (2015). GNSS Multipath Detection Using Three-frequency Signal-to-noise Measurements. GPS Solutions. doi:10.1007/s10291-015-0449-1 

GNSS shadow matching: A new positioning techniques for urban canyons using 3D city modelling

GNSS positioning in dense urban areas is poor due to blockage and reflection of signals by the surrounding buildings. This project is investigating the use of 3D city models to improve the positioning accuracy. A new positioning technique, known as shadow matching, has been conceived and tested. This determines position by comparing measured and predicted satellite visibility and is used alongside conventional techniques. Using shadow matching, you can determine which side of a street you are on when conventional positioning is out by tens of metres. Shadow matching has been demonstrated using both recorded GNSS data and in real time on an Android smartphone. Current research is focused on improving the shadow matching algorithm, assessing its performance under a wide range of conditions, and extending it to multi-epoch positioning. Future work will combine shadow matching with other positioning techniques.

Project duration: 2010-2014; Sponsor: UCL and CSC; Researcher: Lei Wang.

Groves, P. D. (2011). Shadow Matching: A New GNSS Positioning Technique for Urban Canyons. Journal of Navigation, 64 (3), 417-430. doi:10.1017/S0373463311000087

Wang, L., Groves, P. D., & Ziebart, M. K. (2011). GNSS Shadow Matching Using a 3D Model of London in Urban Canyons. Proceedings of European Navigation Conference (ENC) 11.

Groves, P. D., Wang, L., & Ziebart, M. (2012). Shadow matching: Improved GNSS accuracy in Urban canyons. GPS World, 23 (2).

Wang, L., Groves, P., & Ziebart, M. (2012). Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models. Journal of Navigation. doi:10.1017/S0373463312000082

Wang, L., Groves, P. D., & Ziebart, M. K. (2012). GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Prediction Scoring. Proceedings of ION GNSS 2012.

Wang, L., Groves, P. D., & Ziebart, M. K. (2013). Shadow matching: Improving Smartphone GNSS positioning in urban environments. Lecture Notes in Electrical Engineering, 245 LNEE, 613-621. doi:10.1007/978-3-642-37407-4-57

Wang, L., Groves, P. D., & Ziebart, M. K. (2013). Urban Positioning on a Smartphone: Real-time Shadow Matching Using GNSS and 3D City Models. Proceedings of ION GNSS+ 2013.

Wang, L., Groves, P. D., & Ziebart, M. K. (2013). Urban Positioning on a Smartphone: Real-time Shadow Matching Using GNSS and 3D City Models. Inside GNSS, Nov/Dec 2013, 44-56

Wang, L. (2014). Kinematic GNSS Shadow Matching Using a Particle Filter. Proceedings of ION GNSS+ 2014.

Wang, L., Groves, P. D., & Ziebart, M. K. (2015). Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments. Journal of navigation. doi: 10.1017/s0373463314000836.

Wang, L. (2015). Investigation of Shadow Matching for GNSS Positioning in Urban Canyons (Doctoral Dissertation). 

Pedestrian Motion Analysis and Modelling in Support of Indoor and Outdoor Positioning

Positioning and location awareness for pedestrians is becoming increasingly important. However, the technology is immature and there is a need for rigorous testing. This project is studying the effect of pedestrian motion on the performance of common navigation technology, such as GNSS. This will inform the design of both future positioning technology for pedestrian positioning and simulation equipment for testing that technology. Key questions include: What types of motion have a significant impact? What is the effect of the sensor location on the body? Do different individuals affect sensors differently? and What is the dependence on the activity? To answer these, real motion data is being captured using inertial sensors and used to assess the response of GNSS signal-tracking loops to the motion.

Project duration: 2011-2015; Sponsor: EPSRC and Spirent; Researcher: Kimon Voutsis.

Voutsis, K., Groves, P. D., Holbrow, M., & Ford, C. (2014). The Importance of Human Motion for Simulation Testing of GNSS. Proceedings of ION GNSS+ 2014.

Better navigation performance using ultra-low-cost inertial sensors

Consumer-grade micro-electro-mechanical-systems (MEMS) inertial sensors are small, lightweight and low-cost in their raw form. A set of three accelerometers and three gyroscopes costs less than £100. A calibrated inertial measurement unit (IMU) containing the same sensors gives much better performance, but costs over £1000. This project is therefore investigating methods of getting better performance out of the raw sensors, focusing on two main themes. The first is intelligent sensor arrays, containing multiple inertial sensor triads. This technique could produce better performance than simple averaging by exploiting properties of MEMS sensors. These include common-mode errors of different sensors of the same design, the different characteristics of in-plane and out-of-plane sensors, and the complementary properties of sensors with different measurement ranges. The second theme is a user-conducted calibration scheme as part of a multi-sensor integrated navigation system. This would give most of the performance benefit of the expensive laboratory calibration at a much lower cost, by supplying the user with an un-calibrated set of sensors but including software that would calibrate them using a set of simple maneuvers. 

Project duration: 2011-2015; Sponsor: EPSRC and BAE Systems, Researcher: Henry Martin.

Martin, H. F. S., Groves, P. D., Newman, M., & Faragher, R. (2013). A New Approach to Better Low-Cost MEMS IMU Performance Using Sensor Arrays. Proceedings of ION GNSS+ 2013.

Martin, H., Groves, P. D., & Newman, M. (2014). The Limits of In-run Calibration of MEMS and the Effect of New Techniques. Proceedings of ION GNSS+ 2014.

Positioning using location signatures from multiple environment sensors

This project is investigating and developing innovative location and navigation technology based on the novel concept of location signatures. These are local spatial variations of environmental features such as terrain height, magnetic fields, road signs and road surface irregularities, and can be measured using low-cost sensors. This new technique will mitigate GNSS vulnerabilities for mission-critical applications, such as tracking emergency service vehicles and high-value assets. The necessary location-signature database can be generated by users as part of their day-to-day operations and shared cooperatively, removing the need for expensive surveys and keeping it up to date.

Project duration: 2012-2016; Sponsor: EPSRC and Terrafix, Researcher: Debbie Walter.

Walter, D. J., Groves, P. D., Mason, R. J., Harrison, J., Woodward, J., & Wright, P. (2013). Novel Environmental Features for Robust Multisensor Navigation. Proceedings of ION GNSS+ 2013.

Walter, D., Groves, P. D., Mason, B., Harrison, J., Woodward, J., & Wright, P. (2015). Road Navigation Using Multiple Dissimilar Environmental Features To Bridge GNSS Outages. Proceedings of ION GNSS+ 2015.

Better mobile positioning using advanced 3D mapping to enhance satellite navigation

This project will focus on using building reflectivity information to improve GNSS positioning in urban areas. The effect of building reflectivity and topology on both shadow matching and conventional GPS positioning will be measured. This will then be used to develop and test both advanced positioning algorithms and new data formats for 3D mapping. Different ways of collecting the reflectivity data, including dual-polarisation GPS, laser scanning and image-based techniques, will be assessed.

Project duration: 2013-2017; Sponsor: EPSRC, Researcher: Haraldur Gunnarsson.

Intelligent Positioning for Cities

Intelligent urban positioning (IUP) combines multi-constellation GNSS with 3D mapping and potentially other sensors to achieve more accurate and reliable positioning in places where large buildings block and reflect a lot of the GNSS signals. There are many different ways of realising the IUP concept. Under the Intelligent Positioning for Cities (IPC) project, we are investigating a combination of GNSS shadow matching and conventional GNSS ranging with terrain height aiding. In addition, we will explore the use of 3D mapping to make the conventional ranging-based GNSS solution more resilient against NLOS reception and possibly multipath interference. Testing will be conducted with geodetic, consumer and smartphone receivers under a range of different user scenarios and environments.

UCL Engineering Space Geodesy and Navigation Group (SGNL) have hosted a seminar as an opportunity for researchers working in the field of urban positioning or navigation and representative from the user community to share their expertise through one day of formal presentations and informal discussions. More details can be found by clicking on the following link: IPC Seminar 2015

Preliminary activity started: 2012.
Project duration: 2014-2017; Sponsor EPSRC, Researcher: Dr Mounir Adjrad

Groves, P. D., Jiang, Z., Wang, L., & Ziebart, M. K. (2012). Intelligent Urban Positioning using Multi-Constellation GNSS with 3D Mapping and NLOS Signal Detection. Proceedings of ION GNSS 2012.

Groves, P. D., Jiang, Z., Wang, L., & Ziebart, M. (2012). Intelligent urban positioning, shadow matching and non-line-of-sight signal detection. Proceedings of NAVITEC 2012 and European Workshop on GNSS Signals and Signal Processing. doi:10.1109/NAVITEC.2012.6423047.

Adjrad, M and Groves, P. D., & Ellul, C. (2015). Enhancing GNSS Positioning With 3D Mapping. Proceedings of RIN INC 2015.

Groves, P. D., Wang, L., Adjrad, M., & Ellul, C. (2015). GNSS Shadow Matching: The Challenges Ahead. Proceedings of ION GNSS+ 2015.

Adjrad M & Groves, P. D. (2015). Enhancing Conventional GNSS Positioning with 3D Mapping Without Accurate Prior Knowledge. Proceedings of ION GNSS+ 2015.

Context detection, categorization and connectivity for advanced adaptive integrated navigation

Context is the environment that a navigation system operates in (e.g. indoor/outdoor), and the behaviour of its host vehicle or user (e.g. a moving pedestrian or a static car). Many new positioning and navigation techniques, such as GNSS shadow matching, pedestrian dead reckoning using step detection and vehicle motion constraints, are designed to operate only within a particular context. At the same time, there is a move towards systems, such as smartphones, that operate in a variety of different contexts. For best performance, the navigation system must detect its operating context and reconfigure its algorithms accordingly. This is context-adaptive navigation. Context-detection experiments have been conducted and a conceptual framework for a context-adaptive positioning system developed, including a framework for the categorization of different contexts and the introduction of the concepts of connectivity, association and scope. The next stage of research will develop this into a practical context determination system for navigation.

Preliminary activity started: 2013.
Project duration: 2014-2018; Sponsor: UCL and China Scholarship Council, Researcher: Han Gao

Groves, P. D., Martin, H. F. S., Voutsis, K., Walter, D. J., & Wang, L. (2013). Context Detection, Categorization and Connectivity for Advanced Adaptive Integrated Navigation. Proceedings of ION GNSS+ 2013.

Groves, P. D., Wang, L., Walter, D., Martin, H., Voutsis, K., & Jiang, Z. (2014). The Four Key Challenges of Advanced Multisensor Navigation and Positioning. Proceedings of IEEE/ION Position, Location and Navigation Symposium (PLANS)

Future integration architectures for complex multi-sensor navigation and positioning systems

The navigation and positioning community is booming with new technologies to improve both accuracy and reliability. However, integrating them into multisensor systems introduces a number of problems, including how to find the necessary expertise to integrate a diverse range of technologies, how to combine technologies from different organisations that wish to protect their intellectual property, and how to incorporate new navigation technologies and methods without having to redesign the whole system. To address this, a feasibility study has been conducted into a modular approach to the design and development of multisensor integrated navigation and positioning systems. Further issues to address with the new generation of positioning technologies include how to handle ambiguous measurements and how to manage large amounts of landmark data.

Activity started: 2013.

Groves, P. D. (2013). The PNT Boom: Future Trends in Integrated Navigation (Inside GNSS magazine, March-April 2013).

Groves, P. D. (2014). The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution. Journal of Navigation, 67(2), 311-326. doi:10.1017/S0373463313000696

Groves, P. D., Wang, L., Walter, D., Martin, H., Voutsis, K., & Jiang, Z. (2014). The Four Key Challenges of Advanced Multisensor Navigation and Positioning. Proceedings of IEEE/ION Position, Location and Navigation Symposium (PLANS)

Differential positioning by modulation correlation using signals of opportunity

A new differential positioning technique has been developed that operates in the signal domain as opposed to the range or position domain. Thus, measurements of a signal made at two different locations are directly correlated with each other to determine the signal’s time difference of arrival (TDOA) between the two locations. This enables differential ranging to be performed without prior knowledge of the signal’s modulation, opening up new signals of opportunity to positioning. The technique has been successfully demonstrated using medium-frequency AM broadcast signals.

Project duration: 2008-2012; Sponsor: EPSRC, Terrafix, Researcher: Toby Webb.

Webb, T. A., Groves, P. D., Cross, P. A., Mason, R. J., & Harrison, J. H. (2010). A New Differential Positioning Method using Modulation Correlation of Signals of Opportunity. Proceedings of IEEE/ION PLANS 2020, 866-875.

Webb, T. A., Groves, P. D., Mason, R. J., & Harrison, J. H. (2011). A new differential positioning technique applicable to generic FDMA signals of opportunity. Proceedings of ION GNSS 2011, 3527-3538.

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