Robust and low complexity Bayesian data fusion for hybrid cooperative vehicular localization

This paper addresses Particle Filter (PF)-based hybrid Cooperative Localization (CLoc) strategies consisting of fusing absolute position information from embedded Global Navigation Satellite System (GNSS) with relative distance-dependent estimates using Impulse Radio - Ultra WideBand (IR-UWB) technology. Such hybrid GNSS/IR-UWB CLoc yet cannot benefit from the high precision estimates from the IR-UWB due to the disparity between GNSS position and IR-UWB V2V ranging noises, leading to a divergence in CLoc accuracy. This paper first investigates the source of such counter-intuitive effect, and second proposes a novel adaptive Bayesian dithering technique to improve the efficiency of GNSS/IR-UWB fusion-based CLoc. This strategy increases the probability to reach a 20 cm accuracy from 50% (conventional IR-UWB and WiFi PF) to 90%.