Adaptive tracking of non-ellipsoidal extended target with varying number of sub-objects based on variational Bayesian.

High-resolution sensors make it less difficult to detect the true shape of an extended target, and the method of using a single ellipsoid to approximate the extended state of the target is no longer applicable. We can use multiple ellipsoids to approximate the shape of non-ellipsoidal extended targets, but the method of fixing number of sub-objects again fails due to changes in the target shape. In this work, we propose a novel variational Bayesian adaptive filter for non-ellipsoidal extended target tracking (NETT) with a varying number of sub-objects called VN-NETT-VBAF. The proposed filter is used to estimate the motion, extended states, and mixture probabilities of sub-objects describing a non-ellipsoidal extended target. First, the framework of VN-NETT-VBAF is established based on the spawning and combination of sub-objects. Second, the inverse Gamma (IG) distribution is used to model the unknown measurement noise covariance. Based on the variational Bayesian theory, the joint posterior probability density function (PDF) of the system parameters is iteratively updated to achieve the joint estimation of the extended target state and the measurement noise covariance. Finally, an adaptive forgetting factor is designed to update the noise covariance to further improve the robustness of the algorithm to abrupt noise. The simulation results show that the algorithm in this paper can reasonably change the number of sub-objects to adapt to the change of the target extended state, and effectively deal with the abrupt noise to obtain higher filtering accuracy. [ABSTRACT FROM AUTHOR]