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

摘要： 针对多传感器环境下具有形状信息的扩展/群目标跟踪问题，提出了两种融合算法，即高斯逆韦氏并行PHD滤波算法和高斯逆韦氏序贯PHD滤波算法。新算法分别结合并行滤波和序贯滤波算法思想，能够对扩展/群目标的质心状态进行跟踪，对形状进行有效估计。高斯逆韦氏并行PHD滤波算法将各个传感器产生的量测集合并到一个量测集中，统一对量测集进行划分。在滤波更新阶段，对划分后的量测集进行扩维，从而在形式上将多传感器环境下的跟踪问题转化为单传感器环境下的跟踪问题。高斯逆韦氏序贯PHD滤波算法则先对各个传感器产生的量测集依次进行划分，再依次对每一个划分后的量测集进行滤波，从而达到融合多个传感器量测的目的。仿真结果表明该算法的可行性和有效性。

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