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WiFi Fingerprinting Localization for Intelligent Vehicles in Car Park

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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Contributor : Dinh Van Nguyen Connect in order to contact the contributor
Submitted on : Monday, July 30, 2018 - 12:03:14 PM
Last modification on : Tuesday, January 11, 2022 - 11:16:05 AM
Long-term archiving on: : Wednesday, October 31, 2018 - 1:01:38 PM


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  • HAL Id : hal-01851504, version 1



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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