Section: New Results

WiFi Fingerprinting Localization for Intelligent Vehicles in Car Park

Participants : Dinh-Van Nguyen, Raoul de Charette, Fawzi Nashashibi.

A novel method of WiFi fingerprinting for localizing intelligent vehicles in GPS-denied area, such as car parks, has been 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 (cf. [29]).

A more complete study on the use of Wifi fingerprinting for solving the localization problem for autonomous vehicles in GPS-denied environments is presented in the thesis manuscript entitled "Wireless Sensors Networks for Indoor Mapping and Accurate Localization for Low Speed Navigation in Smart Cities" (cf. [11]).