GARDSim - A GPS Receiver Simulation Environment for Integrated Navigation System Development and Analysis

Airservices Australia has recently proposed the use of a Ground-based Regional Augmentation System (GRAS) to improve the safety of using the NAVSTAR Global Positioning System (GPS) in aviation. The GRAS Airborne Receiver Development project (GARD) is being conducted by QUT in conjunction with Airservices Australia and GPSat Systems. The aim of the project is to further enhance the safety and reliability of GPS and GRAS by incorporating smart sensor technology including advanced GPS signal processing and Micro-Electro-Mechanical-Sensor (MEMS) based inertial components. GARDSim is a GPS and GRAS receiver simulation environment which has been developed for algorithm development and analysis in the GARD project. GARDSim is capable of simulating any flight path using a given aeroplane flight model, simulating various GPS, GRAS and inertial system measurements and performing high integrity navigation solutions for the flight. This paper discusses the architecture and capabilities of GARDSim. Simulation results will be presented to demonstrate the usefulness of GARDSim as a simulation environment for algorithm development and evaluation

Robust Positioning in the Presence of Multipath and NLOS GNSS Signals

GNSS signals can be blocked and reflected by nearby objects, such as buildings, walls, and vehicles. They can also be reflected by the ground and by water. These effects are the dominant source of GNSS positioning errors in dense urban environments, though they can have an impact almost anywhere. Non- line-of-sight (NLOS) reception occurs when the direct path from the transmitter to the receiver is blocked and signals are received only via a reflected path. Multipath interference occurs, as the name suggests, when a signal is received via multiple paths. This can be via the direct path and one or more reflected paths, or it can be via multiple reflected paths. As their error characteristics are different, NLOS and multipath interference typically require different mitigation techniques, though some techniques are applicable to both. Antenna design and advanced receiver signal processing techniques can substantially reduce multipath errors. Unless an antenna array is used, NLOS reception has to be detected using the receiver's ranging and carrier-power-to-noise-density ratio (C/N0) measurements and mitigated within the positioning algorithm. Some NLOS mitigation techniques can also be used to combat severe multipath interference. Multipath interference, but not NLOS reception, can also be mitigated by comparing or combining code and carrier measurements, comparing ranging and C/N0 measurements from signals on different frequencies, and analyzing the time evolution of the ranging and C/N0 measurements

Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network

The global navigation satellite systems (GNSS) play a vital role in transport systems for accurate and consistent vehicle localization. However, GNSS observations can be distorted due to multipath effects and non-line-of-sight (NLOS) receptions in challenging environments such as urban canyons. In such cases, traditional methods to classify and exclude faulty GNSS observations may fail, leading to unreliable state estimation and unsafe system operations. This work proposes a Deep-Learning-based method to detect NLOS receptions and predict GNSS pseudorange errors by analyzing GNSS observations as a spatio-temporal modeling problem. Compared to previous works, we construct a transformer-like attention mechanism to enhance the long short-term memory (LSTM) networks, improving model performance and generalization. For the training and evaluation of the proposed network, we used labeled datasets from the cities of Hong Kong and Aachen. We also introduce a dataset generation process to label the GNSS observations using lidar maps. In experimental studies, we compare the proposed network with a deep-learning-based model and classical machine-learning models. Furthermore, we conduct ablation studies of our network components and integrate the NLOS detection with data out-of-distribution in a state estimator. As a result, our network presents improved precision and recall ratios compared to other models. Additionally, we show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.Comment: Accepted for the IEEE ITSC202

Towards secure & robust PNT for automated systems

This dissertation makes four contributions in support of secure and robust position, navigation, and timing (PNT) for automated systems. The first two relate to PNT security while the latter two address robust positioning for automated ground vehicles. The first contribution is a fundamental theory for provably-secure clock synchronization between two agents in a distributed automated system. All one-way synchronization protocols, such as those based on the Global Positioning System (GPS) and other Global Navigation Satellite Systems (GNSS), are shown to be vulnerable to man-in-the-middle delay attacks. This contribution is the first to identify the necessary and sufficient conditions for provably secure clock synchronization. The second contribution, also related to PNT security, is a three-year study of the world-wide GPS interference landscape based on data from a dual-frequency GNSS receiver operating continuously on the International Space Station (ISS). This work is the first publicly-reported space-based survey of GNSS interference, and unveils previously-unreported GNSS interference activity. The third contribution is a novel ground vehicle positioning technique that is robust to GNSS signal blockage, poor lighting conditions, and adverse weather events such as heavy rain and dense fog. The technique relies on sensors that are commonly available on automated vehicles and are insensitive to lighting and inclement weather: automotive radar, low-cost inertial measurement units (IMUs), and GNSS. Remarkably, it is shown that, given a prior radar map, the proposed technique operating on data from off-the-shelf all-weather automotive sensors can maintain sub-50-cm horizontal position accuracy during 60 min of GNSS-denied driving in downtown Austin, TX. This dissertation’s final contribution is an analysis and demonstration of the feasibility of crowd-sourced digital mapping for automated vehicles. Localization techniques, such as the one described in the previous contribution, rely on such digital maps for accuracy and robustness. A key enabler for large-scale up-to-date maps is enlisting the help of the very consumer vehicles that need the map to build and update it. A method for fusing multi-session vision data into a unified digital map is developed. The asymptotic limit of such a map’s globally-referenced position accuracy is explored for the case in which the mapping agents rely on low-cost GNSS receivers performing standard code-phase-based navigation. Experimental validation along a semi-urban route shows that low-cost consumer vehicles incrementally tighten the accuracy of the jointly-optimized digital map over time enough to support sub-lane-level positioning in a global frame of reference.Electrical and Computer Engineerin

Outlier Detection for 3D-Mapping-Aided GNSS Positioning

This paper takes 3D-mapping-aided (3DMA) GNSS as an example and investigates the outlier detection for pattern matching based positioning. Three different test statistics, two in the measurement domain and one in the position domain, are presented. Two 3D city maps with different levels of detail were used, one of which contained two obvious errors, to demonstrate the performance of 3DMA GNSS positioning in the presence of errors in the mapping data. The experiments tested were conducted alongside busy roads in the London Borough of Camden, where a total of 8 sets of 2-minute static pedestrian navigation data were collected with a u-blox EVK M8T GNSS receiver. The results confirm that both 3D mapping errors and temporary environmental changes (such as passing vehicles) can have a significant negative impact on the performance of 3DMA GNSS positioning. After applying outlier detection, single-epoch 3DMA GNSS algorithm reduces the horizontal RMS position error by approximately 15% compared to that without outlier detection. The filtering algorithm attenuates the effects of temporary environmental changes, providing an improvement of about 15% over single-epoch positioning, while the outlier algorithm further reduces the RMS error to a comparable level to that of using high-accuracy maps, about 4.7m

GNSS Vulnerabilities and Existing Solutions:A Review of the Literature

This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS

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

Urban Positioning on a Smartphone: Real-time Shadow Matching Using GNSS and 3D City Models

The performance of global navigation satellite system (GNSS) user equipment in urban canyons is particularly poor in the cross-street direction. This is because more signals are blocked by buildings in the cross-street direction than along the street [1]. To address this problem, shadow matching has been proposed to improve cross-street positioning from street-level to lane-level (meters-level) accuracy using 3D city models. This is a new positioning method that uses the city model to predict which satellites are visible from different locations and then compares this with the measured satellite visibility to determine position [2]. In previous work, we have demonstrated shadow matching using GPS and GLONASS data recorded using a geodetic GNSS receiver in Central London, achieving a cross-street position accuracy within 5m 89% of the time [3]. This paper describes the first real-time implementation of shadow matching on a smartphone capable of receiving both GPS and GLONASS. The typical processing time for the system to provide a solution was between 1 and 2 seconds. On average, the cross-street position accuracy from shadow matching was a factor of four better than the phone’s conventional GNSS position solution. A number of groups have also used 3D city models to predict and, in some cases, correct non-line-of-sight reception [4-6]. However, to our knowledge, this paper reports the first ever demonstration of any 3D-model-aided GNSS positioning technique in real time, as opposed to using recorded GNSS data. When it comes to real-time positioning on a smartphone, various obstacles exist including lower-grade GNSS receivers, limited availability of computational power, memory, and battery power. To tackle these problems, in this work, an efficient smartphone-based shadow-matching positioning system was designed. The system was then implemented in an app (i.e. application or software) on the Android operating system, the most common operating system for smartphones. The app has been developed in Java using Eclipse, a software development environment (SDE). It was built on Standard Android platform 4.0.3, using the Android Application programming interface (API) to retrieve information from the GNSS chip. The new positioning system does not require any additional hardware or real-time rendering of 3D scenes. Instead, a grid of building boundaries is computed in advance and stored within the phone. This grid could also be downloaded from the network on demand. Shadow matching is therefore both power-efficient and cost-effective. Experimental testing was performed in Central London using a Samsung Galaxy S3 smartphone. This receives both GPS and GLONASS satellites and has an assisted GNSS (AGNSS) capability. A 3D city model of the Aldgate area of central London, supplied by ZMapping Ltd, was used. Four experimental locations with different building topologies were selected on Fenchurch Street, a dense urban area. Using the Android app developed in this work, real-time shadow-matching positioning was performed over 6 minutes at each site with a new position solution computed every 5 seconds using both GPS and GLONASS observations were used for real-time positioning. The measurement data was also recorded at 1-second intervals for later analysis. Various criteria are applied to access the new system and compare it with the conventional GNSS positioning results. The experimental results show that the proposed system outperforms the conventional GNSS positioning solution, reducing the mean absolute deviation of the cross-street positioning error from 14.81 m to 3.33 m, with a 77.5 percentage reduction. The feasibility of deploying the new system on a larger scale is also discussed from three perspectives: the availability of 3D city models and satellite information, data storage and transfer requirements, and demand from applications. This meters-level across-street accuracy in urban areas benefits a variety of applications from Intelligent Transportation Systems (ITS) and land navigation systems for automated lane identification to step-by-step guidance for the visually impaired and for tourists, location-based advertisement (LBA) for targeting suitable consumers and many other location-based services (LBS). The system is also expandable to work with Galileo and Beidou (Compass) in the future, with potentially improved performance. In the future, the shadow-matching system can be implemented on a smartphone, a PND, or other consumer-grade navigation device, as part of an intelligent positioning system [7], along with height-aided conventional GNSS positioning, and potentially other technologies, such as Wi-Fi and inertial sensors to give the best overall positioning performance. / References [1] Wang, L., Groves, P. D. & Ziebart, M. Multi-constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models. Journal of Navigation, July 2012. [2] Groves, P. D. 2011. Shadow Matching: A New GNSS Positioning Technique for Urban Canyons The Journal of Navigation, 64, pp417-430. [3] Wang, L., Groves, P. D. & Ziebart, M. K. GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Prediction Scoring. ION GNSS 2012. [4] Obst, M., Bauer, S. and Wanielik, G. Urban Multipath Detection and mitigation with Dynamic 3D Maps for Reliable Land Vehicle Localization. IEEE/ION PLANS 2012. [5] Peyraud, S., Bétaille, D., Renault, S., Ortiz, M., Mougel, F., Meizel, D. and Peyret, F. (2013) About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm. Sensors, Vol. 13, 2013, 829?847. [6] Bourdeau, A., M. Sahmoudi, and J.-Y. Tourneret, “Tight Integration of GNSS and a 3D City Model for Robust Positioning in Urban Canyons,” Proc. ION GNSS 2012. [7] Groves, P. D., Jiang, Z., Wang, L. & Ziebart, M. Intelligent Urban Positioning using Multi-Constellation GNSS with 3D Mapping and NLOS Signal Detection. ION GNSS 2012

Determination of Optimal Antenna Height and Mask Angle of Global Positioning System (GPS) Receiver for Point Positioning

Global Position System (GPS) receivers are capable of surveying measurement with geodetic positioning accuracy. They are designed to be mounted on a tripod usually with a tribrach and set up for observations with assigned mask angle and height of antenna above the survey marks. Assignation of inaccurate mask angle and height of antenna, during observations, is undoubtedly the most prevalent and frequent biases in GPS set up during control surveying which usually leads to less accurate geodetic positioning. Other errors in GPS observations which have been adequately addressed by many researchers are due to ionospheric, stratospheric, tropospheric, and multipath delays among others. Therefore, in this research, an attempt has been made to reduce the arbitrary assignation of GPS mask angle and antenna angle to the barest minimum in order to optimise the accuracy of GPS geodetic positioning. That is, optimal antenna height and mask angle of GPS antenna were determined for geodetic point positioning to be adopted for all GPS observations. The objectives were the determination of the positional coordinates of survey mark at different GPS antenna heights by varying the height upward at 1cm interval with constant mask angle until the optimum height was achieved and determination of the coordinates of survey mark at different mask angle by varying the mask angle upward at 10 interval with the constant optimum height until the optimum mask angle was achieved. The survey mark used was a known point (control point with existing coordinates) within the Federal University of Technology, Akure using Tersus GPS equipment receiver in static mode of observation. Seventy-six (76) coordinate observations were carried out for the determination of optimum antennal height while twenty-three (23) coordinates were observed for the determination of optimum mask angle. The observed field data was post processed using Tersus Geo office processor software and the results obtained were analysed statistically. The values of antenna heights and mask angles were plotted against their respective level of errors. It was observed that 1.76m is the antenna height with the least error level while 200 is the mask angle with the least level of error. Statistical analysis using hypothesis test carried out on the results showed that there is a significant difference in the level of accuracy between the use of 15˚ mask angle as recommended by the manufacturer of the GPS and the of 20˚ mask angle obtained from this research at 95% confidence level. Therefore, it can be concluded that, in a fairly level terrain, 20˚ and 1.76m have proved to be the optimal mask angle and antenna height respectively for all GPS observations during point positioning. Hence, it is recommended that the results obtained in this research should be adopted for GPS observations for better geodetic point positioning