Labeled box-particle CPHD filter for multiple extended targets tracking

In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter. Based on our recent work on extended box-particle probability hypothesis density (ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density (ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density (PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.