A Variational Bayesian Based Strong Tracking Interpolatory Cubature Kalman Filter for Maneuvering Target Tracking

In tracking a maneuverable target, a proper estimation method with better filtering accuracy, stronger robustness, and faster convergence speed is crucial to the tracking system. The performance of conventional nonlinear Gaussian approximate filters may decline when the target engages in abrupt state changes or the noise covariance matrix is unknown and time-varying. In order to overcome these problems, a new variational Bayesian-based strong tracking interpolatory cubature Kalman filter (VB-STICKF) is deduced in this paper. Gaussian weighted integrals in the nonlinear filter are performed using the interpolatory cubature rule, which has better numerical characteristics for maneuvering target tracking. Moreover, by introducing the strong tracking filter into ICKF, the fading factor is used to modify the predicted error covariance, and the residual sequences are forced to be orthogonal, thus the decreasing performance resulted by the states change and the uncertain process noise could be effectively prevented. Furthermore, the measurement noise can be estimated online by variational Bayesian approach based on the inverse wishart distribution, the robustness of dealing with the uncertain measurement noise is improved. The detailed derivation of VB-STICKF for the general nonlinear models is presented in the paper. A target tracking problem with model uncertainty and time-varying process and measurement noise is utilized to test the performance of the proposed filter, the experimental results of three different scenarios demonstrate the improved filtering performance of the deduced VB-STICKF algorithm.