多目标跟踪中一种改进的高斯混合PHD滤波算法

高斯混合概率假设密度（GM-PHD）滤波是一种杂波环境下多目标跟踪问题算法，针对算法中存在的目标漏检和距离相近时精度下降的问题，提出一种改进的高斯混合PHD滤波算法。该算法在高斯混合框架下通过修正PHD递归方程，有效地解决了漏检引起的有用信息丢失问题；利用权值判断高斯分量是否用于提取目标状态，避免了具有较高权值的高斯分量合并在一起，从而改善目标相互接近时的跟踪性能。仿真实验表明，改进算法在滤波精度和目标数估计方面均优于传统的GM-PHD算法。

Improved Gaussian mixture PHD filter for multi-target tracking

The Gaussian mixture probability hypothesis density filter is an algorithm for estimating multiple target states in clutter. An improved algorithm is proposed to resolve the missed detection problem and enhance the accuracy of the filter while tracking close proximity targets. Under Gaussian mixture assumptions, the predication and update equations of the PHD filter are modified, which effectively solve the information loss problem of missed true targets. And then depending on the weights of Gaussian components which decide whether the components can be utilized to extract states, the proposed algorithm avoids the components which have higher weights are merged and improves the tracking performance when the targets move closely. Simulation results show that the new algorithm has advantages over the ordinary one in both the aspects of filter precision and multi-target number estimation.