CAJ | 학술논문

针对概率假设密度(PHD)滤波在杂波环境下对机动多目标进行检测与跟踪时,易出现高阶势分布信息丢失,从而导致目标检测出现偏差的问题,提出一种将势概率假设密度(CPHD)滤波与平滑算法相结合的多目标跟踪算法。从 CPHD的预测与更新步骤出发,结合后向平滑递归公式,推导 CPHD 平滑公式,并提出基于高斯混合实现的 GM-CPHD 平滑器。仿真实验表明,GM-CPHD平滑器的检测与跟踪性能优于未经平滑处理的CPHD滤波器。
침대개솔가설밀도(PHD)려파재잡파배경하대궤동다목표진행검측여근종시,역출현고계세분포신식주실,종이도치목표검측출현편차적문제,제출일충장세개솔가설밀도(CPHD)려파여평활산법상결합적다목표근종산법。종 CPHD적예측여경신보취출발,결합후향평활체귀공식,추도 CPHD 평활공식,병제출기우고사혼합실현적 GM-CPHD 평활기。방진실험표명,GM-CPHD평활기적검측여근종성능우우미경평활처리적CPHD려파기。
When multiple maneuvering targets are estimated and tracked with probability hypothesis density(PHD)filter in clutter,it is easy to lose higher order cardinality information which will result in the estimation deviation of multi-target.A multi-target tracking algorithm combined cardinalized probability hypothesis density (CPHD)filter with the smoothing algo-rithm is proposed.The formula of CPHD smoothing is deduced reasonably according to the prediction,update steps of CPHD and combined with the backward smoothing recursion formula.In addition,the CPHD smoother based on Gaussian mixture is also proposed.Simulation results show that the proposed solution is better than the CPHD filter without smoot-hing.