Adaptive Strong Tracking Square-Root Cubature Kalman Filter for Maneuvering Aircraft Tracking

A novel strong tracking square-root cubature Kalman filter (SCKF) based on the adaptive current statistical (CS) model is proposed aiming at the maneuvering aircraft tracking problem. The Jerk input estimation is introduced on the basis of the modified input estimation algorithm in order to make the connection with the state process noise and the state error covariance matrix. Thus, the online-adaptive adjustment of the CS model is achieved. Additionally, the introduced position of the fading factor is re-deduced and a novel calculation method is designed in order to overcome the invalidity problem of the traditional fading factor. Two simulation scenarios are conducted to verify the effectiveness of the proposed algorithm. The simulation results show that the proposed algorithm possesses better adaptability and tracking precision than the two state-of-the-art single model filters. Moreover, the proposed algorithm decreases the runtime by 40% while maintaining the comparable performance compared with the interacting-multiple-model SCKF.