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

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

NEARCTIS - Role of technologies: Positioning and Communication

Positioning, tracking and communication (PTC) systems are the basic components of cooperative systems, and it is their development in recent years that has in turn made possible the idea of cooperative systems. The ongoing developments of GNSS (Global Navigation Satellite System), of new short range communication systems either between vehicles (V2V) or between the vehicles and the roadside (V2I), lead to new possibilities which have to be taken into account in traffic management. These technologies a primary used for the traffic data collection and the broadcast of information. Secondly we can consider the context of cooperative systems with future use in the verification of traffic strategies, the detection of incidents and the enhancement of driving assistance

Segmentation d'images par combinaison adaptative couleur-texture et classification de pixels. (Applications à la caractérisation de l'environnement de réception de signaux GNSS)

En segmentation d images, les informations de couleur et de texture sont très utilisées. Le premier apport de cette thèse se situe au niveau de l utilisation conjointe de ces deux sources d informations. Nous proposons alors une méthode de combinaison couleur/texture, adaptative et non paramétrique, qui consiste à combiner un (ou plus) gradient couleur et un (ou plus) gradient texture pour ensuite générer un gradient structurel utilisé comme image de potentiel dans l algorithme de croissance de régions par LPE. L originalité de notre méthode réside dans l étude de la dispersion d un nuage de point 3D dans l espace, en utilisant une étude comparative des valeurs propres obtenues par une analyse des composantes principales de la matrice de covariance de ce nuage de points. L approche de combinaison couleur/texture proposée est d abord testée sur deux bases d images, à savoir la base générique d images couleur de BERKELEY et la base d images de texture VISTEX. Cette thèse s inscrivant dans le cadre des projets ViLoc (RFC) et CAPLOC (PREDIT), le deuxième apport de celle-ci se situe au niveau de la caractérisation de l environnement de réception des signaux GNSS pour améliorer le calcul de la position d un mobile en milieu urbain. Dans ce cadre, nous proposons d exclure certains satellites (NLOS dont les signaux sont reçus par réflexion voir totalement bloqués par les obstacles environnants) dans le calcul de la position d un mobile. Deux approches de caractérisation, basées sur le traitement d images, sont alors proposées. La première approche consiste à appliquer la méthode de combinaison couleur/texture proposée sur deux bases d images réelles acquises en mobilité, à l aide d une caméra fisheye installée sur le toit du véhicule de laboratoire, suivie d une classification binaire permettant d obtenir les deux classes d intérêt ciel (signaux LOS) et non ciel (signaux NLOS). Afin de satisfaire la contrainte temps réel exigée par le projet CAPLOC, nous avons proposé une deuxième approche basée sur une simplification de l image couplée à une classification pixellaire adaptée. Le principe d exclusion des satellites NLOS permet d améliorer la précision de la position estimée, mais uniquement lorsque les satellites LOS (dont les signaux sont reçus de manière direct) sont géométriquement bien distribués dans l espace. Dans le but de prendre en compte cette connaissance relative à la distribution des satellites, et par conséquent, améliorer la précision de localisation, nous avons proposé une nouvelle stratégie pour l estimation de position, basée sur l exclusion des satellites NLOS (identifiés par le traitement d images), conditionnée par l information DOP, contenue dans les trames GPS.Color and texture are two main information used in image segmentation. The first contribution of this thesis focuses on the joint use of color and texture information by developing a robust and non parametric method combining color and texture gradients. The proposed color/texture combination allows defining a structural gradient that is used as potential image in watershed algorithm. The originality of the proposed method consists in studying a 3D points cloud generated by color and texture descriptors, followed by an eigenvalue analysis. The color/texture combination method is firstly tested and compared with well known methods in the literature, using two databases (generic BERKELEY database of color images and the VISTEX database of texture images). The applied part of the thesis is within ViLoc project (funded by RFC regional council) and CAPLOC project (funded by PREDIT). In this framework, the second contribution of the thesis concerns the characterization of the environment of GNSS signals reception. In this part, we aim to improve estimated position of a mobile in urban environment by excluding NLOS satellites (for which the signal is masked or received after reflections on obstacles surrounding the antenna environment). For that, we propose two approaches to characterize the environment of GNSS signals reception using image processing. The first one consists in applying the proposed color/texture combination on images acquired in mobility with a fisheye camera located on the roof of a vehicle and oriented toward the sky. The segmentation step is followed by a binary classification to extract two classes sky (LOS signals) and not sky (NLOS signals). The second approach is proposed in order to satisfy the real-time constraint required by the application. This approach is based on image simplification and adaptive pixel classification. The NLOS satellites exclusion principle is interesting, in terms of improving precision of position, when the LOS satellites (for which the signals are received directly) are well geometrically distributed in space. To take into account the knowledge of satellite distribution and then increase the precision of position, we propose a new strategy of position estimation, based on the exclusion of NLOS satellites (identified by the image processing step), conditioned by DOP information, which is provided by GPS data.BELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF

Application of fuzzy theory for identifying the required availability of an autonomous localization unit in European Train Control System

According to the evolution tendency of the control decision process from a trackside to a train-borne system, various autonomous localization units for railway vehicles were developed. As recommended in railway standards, the design process of each system, here the autonomous localization units (LU), follows the V-model whose first step is to define its availability requirement in order to satisfy the global ETCS system requirements. The classical approach for assigning the subsystem availability is based on the assumption that failure parameters of other units are precisely known. This assumption is too restricted in reality due to the lack of information. In this paper, we propose a new approach that allows taking into account uncertainties in the dependability parameters of the ETCS components for identifying the upper threshold of the LU unavailability to reach ETCS availability requirements. Using fuzzy fault trees, the fuzzy unavailability of the ETCS without the autonomous LU is evaluated. Then, based on its membership function, we assess the satisfaction rate that an advanced ETCS with the autonomous LU can satisfy the ETCS availability target

Robust Design of Machine Learning based GNSS NLOS Detector with Multi-Frequency Features

The robust detection of non-line-of-sight (NLOS) signals is of vital importance for land-based and close-to-land safe navigation applications. Their reception and use without adapted mitigation may induce unacceptable inaccuracy and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms have recently shown as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging approach that must however, be tackled carefully to avoid biased estimation results and guarantee generalized algorithms for different scenarios, receivers, antennas and their specific installations and configurations. This work has provided new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focused on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based only on single frequency features. The detection by means of logistic regression not only provides a binary LOS/NLOS decision, but also an associated probability which can be used in the future as a mean to weight specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state of the art, reaching more than 90% detection accuracy in the validation scenarios evaluated

GNSS, IMU, camera and LIDAR technology characterization for railway ground truth and digital map generation

Satellite navigation in combination with affordable onboard sensors are key enabling technologies to support the digitalization in railway transport. However, the adoption of these technologies still requires common methodologies to evaluate anywhere the positioning performance. For this, the comparison with a reference ground truth as well as the availability of digital railway maps is necessary. However, the development of a reliable Ground Truth and Digital Map solutions based on affordable onboard sensors requires on its side first a rigorous characterization of each sensor technology. This paper provides with the most important aspects for the characterization of GNSS, IMU, Camera and LIDAR technologies for its use in railway environment within the context of the European RAILGAP project

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies

Positionner par satellite dans les environnements transports. Impact du canal de propagation sur les performances de localisation GNSS.

Les applications des systèmes de positionnement par satellites (ou GNSS - Global Navigation Satellite System) ont progressivement envahi les systèmes de transport et les applications mobiles. Les applications déployées sont peu exigeantes en termes de performance : elles tolèrent une certaine imprécision et ne requièrent pas nécessairement une grande confiance dans l'information délivrée. Avec l'arrivée des véhicules autonomes ou les évolutions des systèmes de signalisation ferroviaire, de nouveaux besoins sont exprimés : une plus grande précision assortie d'un indicateur de confiance dans l'information fournie, en particulier pour des applications qui mettent en jeu la sécurité des biens et des personnes. Un récepteur GNSS utilise la mesure simultanée de 4 (au moins) temps de propagation des signaux issus de 4 satellites différents pour estimer sa position. Dans les environnements traversés par les systèmes de transports, la réception de ces signaux est fréquemment sujette à des conditions de réception difficiles liées à la présence d'obstacles proches de l'antenne de réception (blocage, réflexion, diffraction) qui engendrent indisponibilité, retards de propagation et donc, erreurs sur le calcul de la position. Pour comprendre les causes et les conséquences des erreurs introduites par le canal de propagation radio sur les performances de localisation, les travaux présentés dans ce mémoire de HDR rassemblent les travaux menés selon 3 axes principaux. Dans ces travaux, nous avons ainsi analysé les effets du canal de propagation radio sur le signal reçu par l'antenne du récepteur GNSS, caractérisé et quantifié la qualité du signal reçu en sortie du récepteur, proposé des méthodes originales permettant de modéliser et pallier les erreurs de localisation et enfin nous avons contribué au développement d'une méthodologie d'analyse de la sûreté de fonctionnement du système de localisation dans le domaine ferroviaire. L'erreur que nous cherchons à réduire en particulier est l'erreur engendrée par les phénomènes de propagation locaux et en particulier par l'utilisation des signaux reçus après réflexions et en l'absence de signal direct, encore appelés NLOS (Non-Line-Of-Sight). Parmi les résultats, nous avons mis en oeuvre des techniques de traitement d'images pour une détection déterministe associée à des techniques d'exclusion et de pondération des signaux reçus. Avec des techniques de traitement du signal, nous avons proposé des méthodes d'estimation dynamique des retards de propagation qui ont montré leur efficacité dans la réduction des imprécisions sur les calculs de la position en milieux urbains. Enfin, nous abordons le concept d'intégrité de la position. Les processus de surveillance de l'intégrité sont aujourd'hui issus de l'aéronautique et s'adaptent mal aux conditions de réception en milieu urbain. Notre connaissance des conditions de propagation des signaux nous a conduits à étudier et à proposer de nouveaux processus pour borner les erreurs de position introduites par l'environnement urbain. Enfin, nous présentons les axes de recherche mis en oeuvre afin de contribuer à l'introduction des solutions GNSS pour des applications de signalisation ferroviaire : nos travaux portent sur l'adaptation des méthodologies classiques ferroviaires au système GNSS et à ses sources de dégradation