A New Algorithm for GPS-Based Vehicle Navigation System

The precise location of a moving vehicle is the one of key factors in intelligent transportation system (ITS). The Kalman filtering technique, which is often applied directly to vehicle navigation system (VNS) operated by GPS, can give optimal estimation of moving vehicles. The navigation accuracy using Kalman filter depends on a reliable function model, stochastic model and proper estimation method. In order to gain good navigation precision, an adaptively robust Kalman filtering algorithm based on the current statistical model is presented in this paper. This filter is a combination of adaptive UD decomposition Kalman filter with quasi-accurate detection of gross errors (QUAD) method. It uses QUAD method to detect and correct gross errors in GPS observations, applies UD decomposition technique to improve computation precision and stability of filter and employs Sage adaptive filter to avoid divergence. Features of good stability, strong adaptability and well elimination of gross errors of this new comprehensive filter have been demonstrated with two simulation examples at last