CAJ | 학술논문

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。
침대다전감기배경하구유형상신식적확전/군목표근종문제，제출료량충융합산법，즉고사역위씨병행PHD려파산법화고사역위씨서관PHD려파산법。신산법분별결합병행려파화서관려파산법사상，능구대확전/군목표적질심상태진행근종，대형상진행유효고계。고사역위씨병행PHD려파산법장각개전감기산생적량측집합병도일개량측집중，통일대량측집진행화분。재려파경신계단，대화분후적량측집진행확유，종이재형식상장다전감기배경하적근종문제전화위단전감기배경하적근종문제。고사역위씨서관PHD려파산법칙선대각개전감기산생적량측집의차진행화분，재의차대매일개화분후적량측집진행려파，종이체도융합다개전감기량측적목적。방진결과표명해산법적가행성화유효성。
Aiming at the problem of extended or group target tracking with shape under multi-sensor environment, two algo-rithms 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 gen-erated by all sensors at the same time are combined into one measurement set, and then this measurement set is parti-tioned. 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.