Reception State Estimation of GNSS satellites in urban environment using particle filtering

International audienceThe reception state of a satellite is an unavailable information for Global Navigation Satellite System receivers. His knowledge or estimation can be used to evaluate the pseudorange. This article deals with the problem using three reception states: direct reception, alternate reception and blocked situation. This parameter, estimated using a Dirichlet distribution, is included in a particle filtering algorithm to improve the GNSS position in urban area. The algorithm takes into account two observation noise models depending on the reception of each satellite. Gaussian probability distribution is used with a direct path whereas a Gaussian mixture model is used in the alternate case

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

Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons

International audienceIn global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such assumption is in some way very restrictive since a random error in the pseudorange measure with an unknown distribution form is always induced in constrained environments especially in urban canyons due to multipath/masking effects. In order to ensure high accuracy positioning, a good estimation of the observation error in these cases is required. To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced. Since the considered positioning problem involves elements of non-Gaussianity and nonlinearity and besides, it should be processed on-line, the suitability of the proposed modeling scheme in a joint state/parameter estimation problem is handled by an efficient Rao-Blackwellized particle filter (RBPF). Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a global navigation satellite system (GNSS)-based localization context where the GPS information may be inaccurate because of hard reception conditions

Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques

In dense urban areas, conventional GNSS does not perform satisfactorily, sometimes resulting in errors of tens of metres. This is due to the blocking, reflection and diffraction of GNSS satellite signals by obstructions such as buildings and moving vehicles. The 3D mapping data of buildings can be used to predict which GNSS signals are line-of-sight (LOS) and which are non-line-of-sight (NLOS). These data have been shown to greatly improve GNSS performance in urban environments. Location-based services typically use single-epoch positioning, while all pedestrian and vehicle navigation applications use filtered solutions. Filtering can reduce the impact of noise-like errors on the position solution. Kalman filtering-based solutions have been adopted as one of the standard algorithms for GNSS navigation in many different products, and particle filtering has been demonstrated by several research groups. This paper mainly investigates the performance of different filtering algorithms combined with 3D-mapping-aided (3DMA) techniques. In addition to the Kalman filter and particle filter, the grid filter is also considered. In contrast to a particle filter, the hypotheses of a grid filter are uniformly distributed (forming a grid), but with different likelihoods, which better fits the physics of the problem. At the same time, this allows the current UCL’s single-epoch 3DMA GNSS positioning algorithm to be easily extended to multi-epoch situations. This paper then compares the performance of these continuous positioning algorithms in urban environments. The datasets used for testing include pedestrian and vehicle navigation data, covering two main application scenarios that often appear in cities. Pedestrian navigation data is static, and was collected in the City of London using a u-blox EVK M8T GNSS receiver. The vehicle navigation data consists of GPS and Galileo measurements, collected in Canary Wharf by a trials van with a Racelogic Labsat 3 GNSS front end. Subsequently, these data are fed into several single- and multi-epoch filtering algorithms, including single-epoch conventional GNSS, single-epoch 3DMA GNSS, conventional extended Kalman Filter (EKF), conventional particle filter (PF), 3DMA GNSS particle filter (PF), and 3DMA GNSS grid filter (GF). The results show that filtering has a greater impact on the results of mobile positioning with significant movement compared to static positioning. In vehicle tests, the conventional multi-epoch GNSS algorithms improve positioning accuracy by more than 40% compared to single-epoch GNSS, whereas in static positioning they deliver a limited improvement. 3DMA GNSS significantly improves positioning accuracy in the denser environments, but provides little benefit in more open areas. The 3DMA GNSS techniques and the filtering algorithms benefit each other. The former provides the latter with a better position solution at the measurement update step, while the latter in turn repays the former with a better initial position and a smaller search area. In vehicle tests at Canary Wharf, the 3DMA GNSS filtering reduces the overall solution error by approximately 50% and 40% compared to the single-epoch 3DMA GNSS and filtered conventional GNSS, respectively. Thus, multi-epoch 3DMA GNSS filtering should bring maximum benefit to mobile positioning in dense environments. The results from both datasets also confirm that the performance of 3DMA GNSS particle filtering and grid filtering are similar in terms of positional accuracy. In terms of efficiency, 3DMA GNSS grid filtering uses fewer particles to achieve the same coverage of the search area as particle filtering

Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques

The performance of different filtering algorithms combined with 3D mapping-aided (3DMA) techniques is investigated in this paper. Several single- and multi-epoch filtering algorithms were implemented and then tested on static pedestrian navigation data collected in the City of London using a u-blox EVK M8T GNSS receiver and vehicle navigation data collected in Canary Wharf, London, by a trial van with a Racelogic Labsat 3 GNSS front-end. The results show that filtering has a greater impact on mobile positioning than static positioning, while 3DMA GNSS brings more significant improvements to positioning accuracy in denser environments than in more open areas. Thus, multi-epoch 3DMA GNSS filtering should bring the maximum benefit to mobile positioning in dense environments. In vehicle tests at Canary Wharf, 3DMA GNSS filtering reduced the RMS horizontal position error by approximately 68% and 57% compared to the single-epoch 3DMA GNSS and filtered conventional GNSS, respectively

Grid-based Hybrid 3DMA GNSS and Terrestrial Positioning

The paper discusses the increasing use of hybridized sensor information for GNSS-based localization and navigation, including the use of 3D map-aided GNSS positioning and terrestrial systems based on different geometric measurement principles. However, both GNSS and terrestrial systems are subject to negative impacts from the propagation environment, which can violate the assumptions of conventionally applied parametric state estimators. Furthermore, dynamic parametric state estimation does not account for multi-modalities within the state space leading to an information loss within the prediction step. In addition, the synchronization of non-deterministic multi-rate measurement systems needs to be accounted. In order to address these challenges, the paper proposes the use of a non-parametric filtering method, specifically a 3DMA multi-epoch Grid Filter, for the tight integration of GNSS and terrestrial signals. Specifically, the fusion of GNSS, Ultra-wide Band (UWB) and vehicle motion data is introduced based on a discrete state representation. Algorithmic challenges, including the use of different measurement models and time synchronization, are addressed. In order to evaluate the proposed method, real-world tests were conducted on an urban automotive testbed in both static and dynamic scenarios. We empirically show that we achieve sub-meter accuracy in the static scenario by averaging a positioning error of $0.64$ m, whereas in the dynamic scenario the average positioning error amounts to $1.62$ m. The paper provides a proof-of-concept of the introduced method and shows the feasibility of the inclusion of terrestrial signals in a 3DMA positioning framework in order to further enhance localization in GNSS-degraded environments

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 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

Kinematic GNSS Shadow Matching Using Particle Filters

Student Paper Award Winner. The poor performance of GNSS user equipment in urban canyons is a well-known problem and is particularly inaccurate in the cross-street direction. However, the accuracy in this direction greatly affects many applications, including vehicle lane identification and high-accuracy pedestrian navigation. Shadow matching was proposed to help solve this problem by using information derived from 3D models of buildings. Though users of GNSS positioning typically move, previous research has focused on static shadow-matching positioning. In this paper, for the first time, kinematic shadow-matching positioning is tackled. Kalman filter based shadow matching is proposed and also, in order to overcome some of its predicted limitations, a particle filter is proposed to better solve the problem