Multi-Model Extension of GM-GGIW-PHD Filter with Jump Markov System for Coexistence Point and Extended Maneuvering Target Tracking

In multi-target tracking, a single model is insufficient to ensure the stable tracking of the maneuvering targets. This paper proposes a multi-model Probability Hypothesis Density (PHD) filtering to address the coexistence of point and extended target (MM-PE-PHD). The multi-model PHD filter utilizes Jump Markov System (JMS) to extend a single motion model to multiple motion models. In most realistic tracking scenarios, the motion model of the target is usually time-varying, and there is often a coexistence of the point target and the extended target. Therefore, it is necessary to perform an adaptive estimation of the target model and build a generalized measurement model to track the coexistence point and extend target. In the MM-PE-PHD algorithm of this paper, the model probabilities of each target and the probability that the target is a point target are also estimated concurrently. To obtain a closed-form solution, the intensity function of PHD is modeled as a mixture of the Gaussian distribution and the Gamma Gaussian Inverse Wishart (GGIW) distribution. Combined with the probability of the point target and model probability, the type of the target, the motion model, the kinematic state, and the extended state are all estimated simultaneously. The simulation experiments validated the effectiveness and superiority of the proposed algorithm.