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A Tracking Algorithm with Weighted Least-squares and Cubature Kalman Filter

Aiming at the problem of passive target tracking, this paper presents a cubature Kalman filter algorithm with weighted least-squares (WLS_CKF) based on time difference of arrival and frequency difference of arrival. The algorithm firstly obtains the initial state of the target through two steps of weighted least-squares, then uses the cubature Kalman filter to complete the nonlinear tracking process. Tracking efficiency and precision are improved by fusing the information of multiple observation stations, multiple measurements and multiple observation time. The simulation results show that, compared with WLS and CKF algorithm, WLS_CKF algorithm can improve the tracking performance significantly.