An improved highly maneuvering target model and adaptive tracking algorithm

This paper investigates an improved current statistic jerk model algorithm to track a highly maneuvering target. In practice, the major difficulty in tracking a highly maneuvering target is the selection of the target motion model and tracking algorithm. Due to the mismatch between the model and the target motion the performance of the tracking filter degrades greatly. In this paper, the jerk time parameter alpha of the current statistic jerk model is adaptively estimated and the jerk maneuvering is assumed to be an exponential correlated stochastic process with non-zero mean. It eliminates the performance limitation of normal jerk model. The adaptive estimation of parameter alpha enhances the tracking precision. The simulation results show the efficiency and feasibility of the proposed algorithm.