多传感器箱粒子PHD滤波多目标跟踪算法

针对目标检测概率较低导致单个传感器无法对目标进行有效检测并跟踪的问题,本文提出了多传感器箱粒子概率假设密度(multi-sensor box particle probability hypothesis density filter,MS-BOX-PHD)滤波器。MS-BOX-PHD滤波器首先将多个传感器的量测转换、融合成为一个量测集合,并利用箱粒子概率假设密度(box particle probability hypothesis density filter,BOX-PHD)滤波器对多个目标的状态进行预测和更新。数值实验表明,相较于单传感器箱粒子概率假设密度(Single-BOX-PHD)滤波器,MS-BOX-PHD滤波器在目标检测概率较低时,能够有效地对多目标的状态和数目进行估计;相较于区间量测下多传感器标准PHD粒子(multi-sensor standard probability hypothesis density particle filter with interval measurement,IM-PHD-PF)滤波器,在达到相同的跟踪性能时,计算效率提升了38.57%。

Multisensor Box Particle PHD Multitarget Tracking Algorithm

As single sensors cannot detect and track targets with low detection probability,a new multisensor box particle probability hypothesis density filter is proposed in this paper.The MS-BOX-PHD filter converts and fuses multiple sensor measurement sets into a new set,and the multitarget states are predicted and updated using a box particle probability hypothesis density filter.Numerical experiments show that the MS-BOX-PHD filter can estimate the state and number of multitargets when the target detection probability is low,unlike a single sensor box particle probability hypothesis density filter.Compared with the multisensor standard probability hypothesis density filter with interval measurement,the computational efficiency increased by 38.57%for the same tracking performance.