WiFi Fingerprinting Localization for Intelligent Vehicles in Car Park

Dinh-Van Nguyen 1, 2 Raoul de Charette 2 Trung-Kien Dao 1, 3 Eric Castelli 1, 3, 4 Fawzi Nashashibi 2
4 PERVASIVE - Interaction située avec les objets et environnements intelligents
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, UGA - Université Grenoble Alpes
Abstract : In this paper, a novel method of WiFi fingerprinting for localizing intelligent vehicles in GPS-denied area, such as car parks, is proposed. Although the method itself is a popular approach for indoor localization application, adapting it to the speed of vehicles requires different treatment. By deploying an ensemble neural network for fingerprinting classification, the method shows a reasonable localization precision at car park speed. Furthermore, a Gaussian Mixture Model (GMM) Particle Filter is applied to increase localization frequency as well as accuracy. Experiments show promising results with average localization error of 0.6m.
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https://hal.inria.fr/hal-01851504
Contributor : Dinh Van Nguyen <>
Submitted on : Monday, July 30, 2018 - 12:03:14 PM
Last modification on : Monday, October 7, 2019 - 4:32:31 PM
Long-term archiving on : Wednesday, October 31, 2018 - 1:01:38 PM

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Dinh-Van Nguyen, Raoul de Charette, Trung-Kien Dao, Eric Castelli, Fawzi Nashashibi. WiFi Fingerprinting Localization for Intelligent Vehicles in Car Park. IPIN 2018 : Ninth International Conference on Indoor Positioning and Indoor Navigation, Sep 2018, Nantes, France. ⟨hal-01851504⟩

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