Box-particle CPHD filter for multi-target tracking
Meng Liang, Liping Song, Hongbing Ji, Yufei Wang · 2015
A novel approach called box-particle cardinalized probability hypothesis density (BP-CPHD) filter for multi-target tracking is proposed in this paper. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space. Box-particle filter is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Furthermore, it decreases the number of particles significantly and reduces the runtime considerably. The proposed algorithm based on box-particle is able to reach a similar accuracy to a SMC-CPHD filter with much less computational costs. Not only does it propagate the PHD, but also propagates the cardinality distribution of target number. Therefore, it generates more accurate and stable instantaneous estimates of the target number and admits more false alarm processes than the box-particle probability hypothesis density (BP-PHD) filter does. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.