关键词: 扩展/群目标跟踪, 概率假设密度滤波, 状态估计, 信息融合

Abstract: Aiming at the problem of extended or group target tracking with shape under multi-sensor environment, two algorithms are proposed, i.e., Gaussian Inverse Wishart Parallel PHD （GIW-PPHD） and Gaussian Inverse Wishart Sequential PHD （GIW-SPHD）. New algorithms combine the ideas of parallel filter and sequential filter respectively. They are both effective to estimate the centroid and the shape of extended or group target. In the GIW-PPHD, the measurement sets generated by all sensors at the same time are combined into one measurement set, and then this measurement set is partitioned. In update stage, the partitioned measurement sets are used to augment the measurement vector, thereby the multi-sensor tracking problem is translated into a single sensor tracking problem. In the GIW-SPHD, the measurements generated by all sensors are partitioned respectively, and then are used to update in sequence. In this manner, the multiple sensors’ measurements are fused all together. Simulation results show that the proposed algorithms are feasible and effective.

Key words: extended or group target tracking, probability hypothesis density filter, sate estimation, information fusion