Multisensor navigation systems: a remedy for GNSS vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required

Investigation of Shadow Matching for GNSS Positioning in Urban Canyons

All travel behavior of people in urban areas relies on knowing their position. Obtaining position has become increasingly easier thanks to the vast popularity of ‘smart’ mobile devices. The main and most accurate positioning technique used in these devices is global navigation satellite systems (GNSS). However, the poor performance of GNSS user equipment in urban canyons is a well-known problem and it is particularly inaccurate in the cross-street direction. The accuracy in this direction greatly affects many applications, including vehicle lane identification and high-accuracy pedestrian navigation. Shadow matching is a new technique that helps solve this problem by integrating GNSS constellation geometries and information derived from 3D models of buildings. This study brings the shadow matching principle from a simple mathematical model, through experimental proof of concept, system design and demonstration, algorithm redesign, comprehensive experimental tests, real-time demonstration and feasibility assessment, to a workable positioning solution. In this thesis, GNSS performance in urban canyons is numerically evaluated using 3D models. Then, a generic two-phase 6-step shadow matching system is proposed, implemented and tested against both geodetic and smartphone-grade GNSS receivers. A Bayesian technique-based shadow matching is proposed to account for NLOS and diffracted signal reception. A particle filter is designed to enable multi-epoch kinematic positioning. Finally, shadow matching is adapted and implemented as a mobile application (app), with feasibility assessment conducted. Results from the investigation confirm that conventional ranging-based GNSS is not adequate for reliable urban positioning. The designed shadow matching positioning system is demonstrated complementary to conventional GNSS in improving urban positioning accuracy. Each of the three generations of shadow matching algorithm is demonstrated to provide better positioning performance, supported by comprehensive experiments. In summary, shadow matching has been demonstrated to significantly improve urban positioning accuracy; it shows great potential to revolutionize urban positioning from street level to lane level, and possibly meter level

GNSS Shadow Matching: The Challenges Ahead

GNSS shadow matching is a new technique that uses 3D mapping to improve positioning accuracy in dense urban areas from tens of meters to within five meters, potentially less. This paper presents the first comprehensive review of shadow matching’s error sources and proposes a program of research and development to take the technology from proof of concept to a robust, reliable and accurate urban positioning product. A summary of the state of the art is also included. Error sources in shadow matching may be divided into six categories: initialization, modelling, propagation, environmental complexity, observation, and algorithm approximations. Performance is also affected by the environmental geometry and it is sometimes necessary to handle solution ambiguity. For each error source, the cause and how it impacts the position solution is explained. Examples are presented, where available, and improvements to the shadow-matching algorithms to mitigate each error are proposed. Methods of accommodating quality control within shadow matching are then proposed, including uncertainty determination, ambiguity detection, and outlier detection. This is followed by a discussion of how shadow matching could be integrated with conventional ranging-based GNSS and other navigation and positioning technologies. This includes a brief review of methods to enhance ranging-based GNSS using 3D mapping. Finally, the practical engineering challenges of shadow matching are assessed, including the system architecture, efficient GNSS signal prediction and the acquisition of 3D mapping data

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

PERFORMANCE EVALUATION OF LOW-COST PRECISION POSITIONING METHODS FOR FUTURE PORT APPLICATIONS

In recent times, a lot of research has been conducted to improve the accuracy of various positioning systems. The motivation behind this trend is to ensure high quality GNSS services for various applications. In particular, emphasis has been placed on improving the level of accuracy of consumer grade GNSS receivers. Significant improvements in the quality of signal reception of these receivers would enable low-cost solutions for asset management in for example, harbor areas. Research in Receiver Autonomous Integrity Monitoring - Fault Detection and Exclusion (RAIM-FDE) algorithms give users the ability to exclude satellites with degraded signals, hereby improving the performance of the GNSS solution. This research investigates and evaluates the performance of various customer grade GNSS positioning systems intended for port applications. Various high precision techniques such as Precise Point Positioning and Real-Time Kinematic were conducted and accuracy levels were noted on Multi-band receivers, Single frequency receivers, and GNSS-enabled smartphone. Our final conclusion suggests optimal low-cost GNSS solutions for asset monitoring and management

Analysis and mitigation of site-dependent effects in static and kinematic GNSS applications

Satellitensignale unterliegen auf ihrem Weg von der Satelliten- zur Empfangsantenne einer Vielzahl an Einflüssen die zu Abweichungen führen. Heutzutage stellen in vielen Anwendungsbereichen insbesondere die stationsspezifischen Anteile, welche sich in Mehrwegeeffekte aus dem Fernfeld, NLOS-Empfang und Signalbeugung, den Einfluss der Satellitengeometrie und Antennennahfeldeffekte untergliedern lassen, einen der genauigkeitsbegrenzenden Faktoren in der satellitengestützten Positionsbestimmung dar. Dies ist dadurch begründet, dass durch die Abhängigkeit von der individuell vorliegenden Antennenumgebung eine Minimierung der Einflüsse erheblich erschwert wird und etablierte Strategien, wie beispielsweise die Differenzbildung in relativen Positionierungsansätzen, in der Regel nicht anwendbar sind. Obwohl diese Effekte bereits seit den frühesten Entwicklungen auf dem Gebiet der satellitengestützten Positionsbestimmung untersucht wurden, ist eine vollumfängliche Lösungsstrategie auch in der heutigen Zeit noch nicht verfügbar. Daher hat diese Thematik nicht an Relevanz verloren und es besteht noch immer der Bedarf an weiteren Untersuchungen zur Vertiefung des Verständnisses und zur Erweiterung des Portfolios an verfügbaren Minimierungsansätzen. In dieser Arbeit werden die vier unterschiedlichen Effekte vor dem Hintergrund der hochpräzisen Positionsbestimmung in statischen und kinematischen GNSS-Anwendungen adressiert. Der wesentliche Fokus der Untersuchungen liegt hierbei auf der Detektion und Elimination betroffener Satellitensignale durch die Einbindung detaillierter Umgebungsmodelle aus terrestrischen Messverfahren. Auf Basis dieser methodischen und empirischen Analysen lassen sich für die einzelnen Effekte vier Hauptaspekte herausstellen: (1) Da Antennennahfeldeffekte primär den Messsensor selbst beeinflussen und folglich die angestrebte Detektion und Elimination zur Minimierung nicht geeignet ist, wird alternativ die Minimierung des Einflusses durch spezielle Antennenaufbauten empirisch analysiert. Daraus resultierend werden mit exakt identischen Antennenaufbauten erreichbare Genauigkeiten im Submillimeterbereich nachgewiesen. (2) Der Einfluss auf die Positionsgenauigkeit der potentiell durch eine Signalelimination hervorgerufenen Verschlechterung der Satellitengeometrie kann durch Simulationen generischer Abschattungsszenarien als unkritisch identifiziert werden. Darüber hinaus wird eine Methode zur Integration der Qualität der Satellitengeometrie in die Wegpunktplanung von UAVs entwickelt, welche sowohl in der Planungsphase, als auch während des UAV-Fluges eine Anpassung und Optimierung der Flugroute ermöglicht. (3) Auf Basis mittels terrestrischer Laserscanner erzeugter Punktwolken wird eine Methode zur Erzeugung von Elevationsmasken entwickelt, welche adaptiv gegenüber der vorliegenden Antennenumgebung sind und eine effektive Detektion und Elimination von Satellitensignalen erlauben, die NLOS-Empfang oder Signalbeugung unterliegen. Diese Minimierungsstrategie ist sowohl in statischen, als auch kinematischen Anwendungen einsetzbar und ermöglicht bei zusätzlicher Einbindung von Fresnel Zonen auch die Berücksichtigung der Ausbreitungseigenschaften elektromagnetischer Wellen. (4) Als vorbereitender Schritt für die Entwicklung von Methoden zur Detektion und Eliminierung von Fernfeld-Mehrwegeeffekten werden die Voraussetzungen für die Entstehung der Effekte untersucht. Durch Vergleich simulierter und beobachteter SNR-Zeitreihen und der Berücksichtigung von Fresnel Zonen kann eine Überlappung von 50% zwischen Fresnel-Zone und Reflektorfläche als bereits ausreichend für eine potentielle Mehrwegebelastung identifiziert werden. In der Gesamtbetrachtung liefern die in dieser Arbeit gewonnenen Erkenntnisse und entwickelten Methoden einen relevanten Beitrag zu dem übergeordneten Ziel einer ganzheitlichen Minimierung stationsspezifischer Abweichungen und ermöglichen so eine signifikante Verbesserung der Positionsgenauigkeit unter schwierigen GNSS-Bedingungen. Darüber hinaus nimmt diese Arbeit den in den letzten Jahren forcierten Trend von einer punktweisen zu einer flächenhaften Objekterfassung an, indem das Potenzial einer detaillierten und effizienten Erfassung der Antennenumgebung mittels terrestrischer Laserscanner zur Minimierung und Analyse stationsspezifischer Abweichungen bei der satellitengestützten Positionsbestimmung aufzeigt und genutzt wird.Satellite signals are subject to various error sources on their way from the satellite to the receiving antenna. Nowadays, in many fields of application, the site-dependent parts, which can be separated into far-field multipath, NLOS reception and signal diffraction, the influence of the satellite geometry and antenna near-field effects, are one of the accuracy limiting factors in satellite-based positioning. This is due to the fact that the dependence on the individual antenna environment considerably impedes a minimization of the influences and established strategies, such as double-differencing in relative positioning approaches, are generally not applicable. Although these effects have been subject to scientific research since the earliest developments in the field of satellite-based positioning, an all-embracing solution is still lacking. Therefore, this topic has not lost its relevance and there is still a need for further investigations to deepen the understanding and expanding the portfolio of available mitigation techniques. In this dissertation, the four different effects are addressed against the background of high-precision static and kinematic GNSS applications. In this context, the main focus of the investigations is on the detection and exclusion of affected satellite signals, by integrating detailed environment models derived from terrestrial measurements. Based on these methodological and empirical analyses, four main aspects can be highlighted for the different effects: (1) Since antenna near-field effects primarily affect the measuring sensor itself, and thus, the striven detection and exclusion for mitigation is not applicable in this case, alternatively the mitigation of the influence by special antenna setups is empirically analyzed. As a result, achievable accuracies in the sub-millimeter range can be demonstrated using exactly identical antenna setups. (2) By simulating generic obstruction scenarios, the influence on the positional accuracy of the deterioration of the satellite geometry, potentially caused by an elimination of satellite signals, can be identified as uncritical. Furthermore, a method for integrating measures for the quality of the satellite geometry in the waypoint planning of UAVs is developed, which enables the adaption and optimization of the flight route in the planning phase, as well as during the UAV flight. (3) Based on point clouds of terrestrial laser scanners, a method for the determination of elevation masks that are adaptive to the present antenna environment is developed, which enables an effective detection and exclusion of signals that are subject to NLOS reception or signal diffraction. This mitigation strategy can be applied to static and kinematic GNSS applications and by additionally integrating Fresnel zones, also the propagation characteristics of electromagnetic waves are considered. (4) As a preparatory step for the development of methods for detecting and excluding far-field multipath, the prerequisites for the occurrence of the effect are investigated. By comparison of simulated and observed SNR time series and by considering Fresnel zones, an overlap of 50% between Fresnel zone and reflecting surface can be identified as already being sufficient for potential far-field multipath influences. In the overall view, the findings and methods developed in this dissertation represent a relevant contribution to the superordinate goal of a holistic mitigation of site-dependent effects, and thus, enable a significant improvement of the positional accuracy under difficult GNSS conditions. In addition, this thesis adopts the currently forced trend from a pointwise to an area-based object acquisition by revealing and exploiting the potential of a detailed and efficient acquisition of the antenna environment by terrestrial laser scanners for mitigating and analyzing site-dependent effects in satellite based positioning applications

Development of GNSS/INS/SLAM Algorithms for Navigation in Constrained Environments

For land vehicles, the requirements of the navigation solution in terms of accuracy, integrity, continuity and availability are more and more stringent, especially with the development of autonomous vehicles. This type of application requires a navigation system not only capable of providing an accurate and reliable position, velocity and attitude solution continuously but also having a reasonable cost. In the last decades, GNSS has been the most widely used navigation system especially with the receivers decreasing cost over the years. However, despite of its capability to provide absolute navigation information with long time accuracy, this system suffers from problems related to signal propagation especially in urban environments where buildings, trees and other structures hinder the reception of GNSS signals and degrade their quality. This can result in significant positioning error exceeding in some cases a kilometer. Many techniques are proposed in the literature to mitigate these problems and improve the GNSS accuracy. Unfortunately, all these techniques have limitations. A possible way to overcome these problems is to fuse “good” GNSS measurements with other sensors having complementary characteristics. In fact, by exploiting the complementarity of sensors, hybridization algorithms can improve the navigation solution compared to solutions provided by each stand-alone sensor. Generally, the most widely implemented hybridization algorithms for land vehicles fuse GNSS measurements with inertial and/or odometric data. Thereby, these Dead-Reckoning (DR) sensors ensure the system continuity when GNSS information is unavailable and improve the system performance when GNSS signals are degraded, and, in return the GNSS limits the drift of the DR solution if it is available. However the performance achieved by this hybridization depends thoroughly on the quality of the DR sensor used especially when GNSS signals are degraded or unavailable. Therefore, this Ph.D. thesis, which is part of a common French research project involving two laboratories and three companies, aims at extending the classical hybridization architecture by including other sensors capable of improving the navigation performances while having a low cost and being easily embeddable. For this reason, the use of vision-based navigation techniques to provide additional information is proposed in this thesis. In fact, cameras have become an attractive positioning sensor recently with the development of Visual Odometry and Simultaneous Localization and Mapping (SLAM) techniques, capable of providing accurate navigation solution while having reasonable cost. In addition, visual navigation solutions have a good quality in textured environments where GNSS is likely to encounter bad performance. Therefore, this work focuses on developing a multi-sensor fusion architecture integrating visual information with the previously mentioned sensors. In particular, the contribution of this information to improve the vision-free navigation system performance is highlighted. The proposed architecture respects the project constraints consisting of developing a versatile and modular low-cost system capable of providing continuously a good navigation solution, where each sensor may be easily discarded when its information should not be used in the navigation solutio

Computational Aspects of Sensor Fusion for GNSS Outlier Mitigation in Navigation

As the Global Navigation Satellite Systems (GNSS) are intensively used as main source of Position, Navigation and Timing (PNT) information for maritime and inland water navigation, it becomes increasingly important to ensure the reliability of GNSS-based navigation solutions for challenging environments. Although an intensive work has been done in developing GNSS Receiver Autonomous Integrity Monitoring (RAIM) algorithms, a reliable procedure to mitigate multiple simultaneous outliers is still lacking. The presented work evaluates the performance of several methods for multiple outlier mitigation based on robust estimation framework and compares them to the performance of state-of-the-art methods. The relevant methods include M-estimation, S-estimation, Least Median of Squares LMS-based approaches as well as corresponding modifications for C/N0-based weighting schemes. The snapshot positioning methods are also tested within the quaternion-based Unscented Kalman filter for integrated inertial/GNSS solution. The proposed schemes are evaluated using real measurement data from challenging inland water scenarios with multiple bridges and a waterway lock. The initial results are encouraging and clearly indicate the potential of the discussed methods both for classical snapshot solutions as well for the methods with complementary sensors