A new Kalman filter and its application in GPS-based vehicle navigation system

Kalman filtering technique can be used as optimal estimation of moving vehicles; it can be applied directly to Vehicle Navigation System (VNS) operated by GPS. The navigation accuracy using Kalman filters combining with GPS depends on a priori knowledge of system models, noise statistics and well elimination of gross errors in GPS data. In order to gain good navigation precision, a new comprehensive Kalman filter 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 observation while using UD decomposition technique to improve computation precision and overcome the instability of filter caused by instability of values. When a divergence of Kalman filter is detected, an adaptive filter will be employed to adjust the prediction error covariance matrix. In a simulation example, the new Kalman filtering method demonstrated features of good stability in computation values, strong adaptability and well elimination of gross errors.