快速多目标跟踪GM-PHD滤波算法

Fast GM-PHD Filter for Multi-target Tracking

传统的GM-PHD(Gaussian Mixture-Probability Hypothesis Density)滤波算法用当前时刻接收到的全部量测值对所有高斯项进行更新,使得大量的运算时间花费在使用无效量测对高斯项的更新上。针对此问题,提出一种快速多目标跟踪GM-PHD滤波器。首先在算法预测步骤中将高斯项分为新生及存活目标两类;然后在更新步骤中先计算存活目标与所有量测之间的残差,使用椭球门限,用门限内的量测值来更新存活目标;接着计算新生目标与剩下量测之间的残差,再次使用落入椭球门限内的量测值来更新新生目标,这样可以最大限度地将无效量测排除掉,从而减少算法运算时间。实验结果表明,该方法在保证目标跟踪精度的同时降低了算法时间复杂度,其综合性能优于传统的GM-PHD滤波算法。

In the traditional GM-PHD filter,all measurements received at current time are used to update different types of targets.Much time is spent on updating targets because of using invalid measurements.A kind of fast multi-target tracking filter was proposed in this paper.Firstly,Gaussian components are divided into two parts.One part is birth targets and the other is survival targets.Then the residuals between survival targets and all measurements are calculated.Next,only the measurements which fall in the elliptical gate are used to update survival targets.Similarly,the residuals between birth targets and remaining measurements are calculated,and only those measurements which fall in the elliptical gate are used to update birth targets.In this way,we could minimize invalid measurements and reduce the computing complexity.The experimental results show that the new method not only reduces the time complexity greatly,but also insures the accuracy of target tracking.Its performance is better than the traditional GM-PHD filter as a whole.