Gaussian Mixture Cardinalized Probability Hypothesis Density Filter for Multiple Maneuvering Target Tracking under Unknown Clutter Situation

Considering the limitation of the well-known multiple model formulation of the Random Finite Set (RFS) that the statistics characteristic of clutter is assumed to be known a priori, this paper proposes a new multiple maneuvering target tracking algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density Filter (GMCPHDF) in the case of unknown clutter. The proposed method predicts the intensity function of actual target states by Best-Fitting Gaussian (BFG) approximation, which is independent of the target motion model. Then the closed-loop iteration procedure among the intensity function of actual target states, the mean number of clutter generators, and the hybrid cardinality distribution of actual targets and clutter generators is established. The simulation results show that the proposed method can effectively estimate the target number, target states and the mean number of clutters simultaneously.