Bayesian Bootstrap Filter for integrated GPS and Dead Reckoning Positioning
Touil Khalid, Mourad Zribi, Choquel Jean-Bernard, Mohammed Benjelloun · 2007
Localization of vehicles in road environments is an important task in the field of developing driver assistance systems. The localization performance of a navigation system can be improved by coupling different types of sensors. In this paper a practical combined positioning model of Global Positioning System (GPS) and dead reckoning (DR) technology is put forward. The measurement results from DR and GPS sensors are fused by using Bayesian bootstrap filtering (BBF). Bootstrap filter is a filtering method based on Bayesian state estimation and Monte Carlo method, which has the great advantage of being able to handle any functional non-linearity and system and/or measurement noise of any distribution. Experimental result demonstrates that the bootstrap filter gives better positions estimate than the standard extended Kalman filter (EKF).