Her research focuses on performance evaluation of integrated navigation system. She worked within Ifsttar for an internship in the summer 2019.Dr. Juliette Marais received the engineering degree from Institut Supérieur de l'Electronique et du Numérique. She received the Ph.D. degree in electronics and the "Habilitation à diriger des recherches" from the University of Lille, France, in 2002 and 2017 respectively. Since 2002, she has been a research fellow with IFSTTAR, the French Institute of science and technology for transport, development, and networks. She is involved on two main research projects: integrity monitoring for land transport applications and GNSS propagation characterization in railway environments, with some contributions in EU or national projects. Her research interests principally include propagation phenomena, positioning and pseudorange error modeling, filtering technics, and simulation. Syed Ali Kazim works as a research engineer at IFSTTAR, the French Institute of science and technology for transport, development, and networks since 2018. His current research interests focus on multipath detection, modelling and mitigation. He is involved in European projects such as ERSAT GGC and Gate4Rail.Nourdine Aït Tmazirte works at French Institute of Technology Railenium since 2018. He got his engineering and M.Sc. degree in automation engineering from Ecole Centrale de Lille, France, both in 2010. His research interests include multi-sensor fault tolerant fusion for localization and integrity assessment. Dr.-Ing. Debiao Lu works at faculty of Electronic and Information Engineering at Beijing Jiaotong University in China since 2015. He got his doctor title at faculty of Mechanical Engineering at Technische Universität Braunschweig, Germany in 2014. He received the B.Sc. degree in telecommunication engineering and M.Sc. degree in traffic information engineering and control, both at Beijing Jiaotong University in China. His research interests include GNSS safe localization, GNSS propagation simulation and performance assessment.ABSTRACT GNSS used as a standalone positioning system fulfils most of the requirements for many applications since decades. However, the need of high available, accurate and fail-safe positioning systems for new applications such as rail autonomous vehicles for train signaling applications motivates the community to explore novel solutions. In this context, multi-constellation multi-frequency GNSS receivers have the potential to enhance the positioning solutions. However, when applied to safety-critical land transportation system, the localization function not only needs to meet accuracy requirements, but more importantly, it also needs to bound the positioning errors in order to reduce the risk of unexpected and undetected faults. Thus, in order to reach continuous and fail-safe positioning solutions, the use of complementary heterogeneous sensors with a smart hybridization becomes essential. For safety-related and regulated applications as in railways, one will also need, as ...
Autonomous vehicle board an increasing quantity of sensors such as GNSS receivers, road maps, LIDARs, to ensure a reliable localization function. This expanding number leads to abandon a classical centralised data fusion method and to adopt more complex architectures such as Distributed Data Fusion. In this paper, we propose an approach to detect faults of the different sensors in such configuration and to dynamically reconfigure the fusion method by using simple concepts of Information Theory. In order to detect faults, consistency is examined through a log likelihood ratio between the information innovation of each sensor. The current study illustrates the performance of the proposed fault detection algorithm and the pertinence of the dynamical reconfiguration of the multi-sensors data fusion. Experimental results, using data from antilock braking system sensors, a differential global positioning system receiver, and an accurate digital roadmap illustrate the performance of the proposed approach.
Faults in the GNSS measurements are the main reason for uncertainty positioning. Accuracy can thus be maximized by selecting only those observations least contaminated by faults to form the navigation solution (positioning) and discarding the rest. In this paper, we propose an algorithm to solve the problem of multi faults in the GNSS observations (pseudorange). The algorithm is based on observations projection on information space in order to detect and exclude the measurement faults and on Information Filter in order to estimate the position. The proposed method is tested using real data acquired with an experimental vehicle using low cost GNSS receiver in order to demonstrate its efficiency and its validity.
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