Strong tracking square-root cubature Kalman filter over adaptive current statistical model

A tracking algorithm is proposed by the integration of the adaptive current statistical (CS) model and the modified square-root cubature Kalman filter (SCKF) aiming at the maneuvering target tracking problem. Firstly, the Jerk input estimation is introduced based on the CS model and the modified input estimation (MIE) algorithm in order to make the connection of the state process noise and the state covariance matrix as well as to realize the adaptive adjustment of the CS model. Secondly, the introduced position of the fading factor is relocated from orthogonality principle and a calculation formula of the fading factor is put forward in order to overcome the invalidation problem of the traditional fading factor in radar measurement coordinate. Thereby, the strong tracking SCKF (ST-SCKF) filter is structured. Additionally, the strong maneuvering detection function is established by utilizing the output of the ST-SCKF to adjust the maneuvering frequency of the adaptive CS model. The simulation results show that the proposed algorithm shows better adaptability and tracking accuracy than the SCKF algorithm based on the CS model and the multiple fading factors, the SCKF-STF algorithm based on the modified CS model as well as the interactive multiple model SCKF (IMM-SCKF) algorithm. Moreover, the real-time property is significantly improved compared with the IMM-SCKF algorithm. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.