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Algorithmes de géolocalisation à l’intérieur d’un bâtiment en temps différé

Kersane Zoubert-Ousseni 1, 2
2 ASPI - Applications of interacting particle systems to statistics
IRMAR - Institut de Recherche Mathématique de Rennes, Inria Rennes – Bretagne Atlantique
Abstract : Real time indoor geolocalization has recently been widely studied, and has many applications. Off-line (post-processing) trajectory estimation also presents some interest. Off-line indoor geolocalization makes it possible for instance to develop crowdsourcing approaches that take advantage of a large number of users to collect a large number of measurements: knowing the trajectory of a smartphone user makes it possible for instance to feed an attendance map. Estimating this trajectory does not need to be performed in real-time and can be performed off-line, two main benefits. Firstly, the real-time approach estimates a current position using present and past measurements only, when the off-line approach has access to the whole measurements, and makes it possible to obtain an estimated trajectory that is smoother and more accurate than with a real-time approach. Secondly, this estimation can be done on a server and does not need to be implemented in the smartphone as it is the case in the real-time approach, with the consequence that more computing power and size memory are available. The objective of this PhD is to provide an off-line estimation of the trajectory of a smartphone user receiving signal strength (RSS) of wifi or bluetooth measurements and collecting inertial measurements (IMU). In the beginning, without the floorplan of the building, a parametric model is proposed, based on an adaptive pathloss model for RSS measurements and on a piecewise parametrization for the inertial trajectory, obtained with IMU measurements. Results are an average error of 6.2mfor the off-line estimation against 12.5m for the real-time estimation. Then, information on displacement constraints induced by the walls is considered, that makes it possible to adjust the estimated trajectory by using a particle technique as often done in the state-of-the-art. With this second approach we developped a particle smoother and a maximum a posteriori estimator using the Viterbi algorithm. Other numerical heuristics have been introduced. A first heuristic makes use of the parametric model developed without the floorplan to adjust the state model of the user which was originally based on IMUalone. A second heuristic proposes to performseveral realization of a particle filter and to define two score functions based on RSS and on the continuity of the estimated trajectory. The scores are then used to select the best realization of the particle filter as the estimated trajectory. A global algorithm, which uses all of the aforementioned approaches, leads to an error of 3.6m against 5.8m in real-time. Lastly, a statistical machine learning model produced with random forests makes it possible to distinguish the correct estimated trajectories by only using few variables to be used in a crowdsourcing framework.
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Kersane Zoubert-Ousseni. Algorithmes de géolocalisation à l’intérieur d’un bâtiment en temps différé. Traitement du signal et de l'image [eess.SP]. Université Rennes 1, 2018. Français. ⟨NNT : 2018REN1S015⟩. ⟨tel-01838480⟩

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