Pseudolite dynamic tracking and positioning algorithm based on square root UKF

Abstract

In order to solve the limitation of traditional Kalman filter in dealing with nonlinear system and the problem of divergence and poor accuracy of extended Kalman filter (EKF) in dealing with strong nonlinear system, a dynamic tracking and positioning algorithm based on square root UKF was proposed based on the unscented Kalman filter (UKF) algorithm in combination with the problem of target tracking and positioning in dynamic navigation system. In the process of recursive operation, the square root of covariance matrix was used to replace the covariance matrix in the calculation process of covariance algorithm. The MATLAB simulation results show that the accuracy of the square root UKF algorithm is 54.7% higher than that of the EKF algorithm, and 14.8% higher than that of the UKF algorithm. The proposed algorithm solves the limitation of Kalman processing nonlinear system and the problem of the low accuracy of traditional EKF and UKF algorithms, and provides a strong support for the high-precision positioning of pseudolite system.

Keywords

• algorithm theory; dynamic positioning and tracking; pseudolite; square root filtering; kalman filter algorithm; nonlinearity