Multitarget association and tracking in 3-D space based on particle filter with joint multitarget probability density

This paper addresses the problem of 3-dimensional (3D) multitarget tracking using particle filter with the joint multitarget probability density (JMPD) technique. The estimation allows the nonlinear target motion with unlabeled measurement association as well as non-Gaussian target state densities. In addition, we decompose the 3D formulation into multiple 2D particle filters that operate on the 2D planes. Both selection and combining of the 2D particle filters for 3D tracking are presented and discussed. Finally, we analyze the tracking and association performance of the proposed approach especially in the cases of multitarget crossing and overlapping.

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