一种具有信息保持能力的GM-PHD滤波器

概率假设密度(PHD)滤波器是解决虚警、漏检和目标数未知情况下多目标跟踪问题的新方法.然而在该滤波器中已存在的目标一旦在某个时刻不能被传感器检测到,漏检目标的大量信息会被滤波器丢弃.为解决漏检目标的信息丢失问题,对PHD滤波器的预测和更新方程进行了修正,提出了一种具有信息保持能力的PHD滤波器.在此基础上提出了适用于线性高斯模型的修正PHD滤波器高斯混合(GM)实现算法.仿真实验结果表明,与现有的PHD滤波器相比,在存在漏检的情况下所提出的GM-PHD滤波器能够提供更好的多目标跟踪能力.

A Gaussian Mixture PHD Filter with the Capability of Information Hold

The probability hypothesis density(PHD)filter has been proved to be an efficient method for the multi-target tracking in the presence of false alarms,missed detections and an unknown number of targets.However,in the original PHD filter,a large amount of information of the existing targets will be immediately discarded by the PHD filter once they cannot be detected by a sensor at a given time.To resolve the information loss problem of missed true targets,we modify the predication and update equations of the PHD filter and propose a modified PHD filter with the capability of information hold.A Gaussian mixture implementation of the modified PHD filter for linear Gaussian models is also presented.The simulation results demonstrate that the proposed filter can achieve better tracking performance of multiple targets than the original PHD filter in the presence of missed detections.