Improved Kalman Filter Based Accurate Pseudo-Range Positioning Method for BeiDou

Abstract

In response to the issue of poor performance in traditional Beidou pseudorange positioning, this paper proposes a Beidou pseudorange precise positioning method that combines weighted least squares and adaptive Kalman filtering based on moving window covariance estimation. This method utilizes the fast convergence speed of the weighted least squares and the high accuracy of Kalman filtering. On this basis, the Kalman filter is modified with a moving window to ensure the accuracy and global convergence of the positioning process. Through experimental simulation and comparison, the effectiveness of this algorithm is demonstrated, showing its ability to improve positioning accuracy and meet certain positioning requirements.