基于AI-AP-PHD滤波的多目标跟踪方法

Multi-target Tracking Based on AI-AP-PHD Filter

目标的幅值信息可提供更多精确的目标和杂波似然函数信息,结合其对多目标进行跟踪可有效改善传感器的检测跟踪性能,构造了一种实际可行的结合目标幅值信息的辅助粒子PHD(AI-AP-PHD)滤波方法。该方法首先对目标状态向量和量测向量进行扩展,然后通过扩展的动态方程和量测方程实现系统的预测和更新,同时给出多目标的数量和状态估计。仿真结果表明,该方法非常适应于目标信噪比未知的情况,且性能优于传统的PHD滤波方法。

The amplitude information of target can provide more accurate target and false-alarm likelihoods,which can be used to improve the performance of multi-target detection and tracking.In this paper,a practical and feasible auxiliary particle probability hypothesis density filter combined with target amplitude information(AI-AP-PHD)is proposed.Firstly,the target state vector and measurement vector are expanded,and then the extended dynamic equation and measurement equation are used to realize the prediction and update of system,the number and state estimation of multi-target are given simultaneously in the end.The simulation results show that the proposed method is very suitable for the situation of unknown target SNR,and perform well than the traditional PHD filter method.