Hybrid filtering for map-aided vehicle navigation

Abstract

In order to provide an accurate positioning, the land-vehicle navigation applications are based on GPS. The addition of a digital road map allows to locate the vehicle continuously and helps the driver to get the best path. These systems are usually enhanced with dead reckoning sensors due to GPS outages in urban areas especially. For instance, the odometer sensors can be used to correct the vehicle location in this case. We present here a global estimation method to solve the fusion problem of the GPS, odometer and digital road map measurements in presence of GPS outages. It relies on a hybrid filter which takes advantage of the combination of a Kalman filter which computes the linear part of the state equations and a particle filter to provide an optimal resolution scheme. When GPS fails, the filter fuses all available pseudo-range measures to improve the vehicle positioning. In the case of an urban transport scenario, the results show that the number of particles is significantly reduced to achieve the same performance of a single particle filter in terms of accuracy. Moreover, software solutions can be developed for real-time applications