Neighbor-Aided Localization in Vehicular Networks

Abstract

We address the problem of localization in vehicular ad hoc networks. Our goal is to leverage vehicle communications and smartphone sensors to improve the overall localization performance. Assuming vehicles are equipped with the IEEE 802.11p wireless interfaces, we employ a two-stage Bayesian filter to track the vehicle's position: an unscented Kalman filter for heading estimation using smartphone inertial sensors, and a particle filter that fuses vehicle-to-vehicle signal strength measurements received from mobile anchors whose positions are uncertain, with velocity, GPS position, and map information. Our model leads to a robust localization system and is able to provide useful position information even in the absence of GPS data. We evaluate the algorithm performance using real-world measurements collected from four communicating vehicles in an urban scenario, and considering different combinations of location information sources.