Vehicle Multi-sensor Information Optimization Based on Federal Fusion Valuation

Abstract

Dead reckoning system (DR) and Global Positioning System (GPS), which consist of integrated navigation system, are two important positioning methods in the intelligent vehicle navigation. The information from the different sensors of vehicle GPS and DR integrated navigation system needs to be fused in order to implement the optimal evaluation of global states, because of the different measurements and their noise characteristics. The federal Kalman filter is designed to fuse GPS and DR information. Two local filters process GPS and DR data respectively, and the main filter is responsible for data fusion and reset to the local filters. The information fusion based on federal filter solves some key problems such as system unavailability, big accumulative errors with GPS or DR alone, and it makes the system's global evaluation optimal. The simulation results show that the positioning accuracy and the credibility of the vehicle integrated navigation are much higher than that when GPS or DR is used alone.