幅度信息辅助的GM-PHD-UKF弱小多目标跟踪算法

An Algorithm of Weak Multi-target Tracking Based on GM-PHD-UKF with Amplitude Information

针对弱小目标雷达散射面积小、回波能量弱、在跟踪过程中虚警杂波数较多,容易产生误跟、漏跟的问题,提出了一种幅度信息辅助的无迹高斯混合概率假设滤波(GM-PHD)算法。该算法通过建立弱小目标与虚警杂波的幅度似然函数,利用幅度信息提升目标与杂波的识别度,改善多目标的状态估计与势估计。此外,该算法还使用无迹卡尔曼滤波(UKF)进行扩展,以适应非线性的跟踪情况。仿真结果表明,相比于传统的高斯混合概率假设滤波,所提算法拥有更佳的多目标状态估计与势估计性能。

In view of small radar scattering area and weak echo energy of weak targetswhich lead to a large number of false-alarm clutters in the tracking process and the problem of false tracking and missed trackingthis paper proposes a GM-PHD algorithm assisted by amplitude information.By establishing amplitude likelihood functions of the weak target and the false-alarm clutterthe algorithm uses the amplitude information to improve the recognition rate of the target and the clutterand improves the state estimation and potential estimation of the multiple targets.In additionthe algorithm is extended by Unscented Kalman Filtering(UKF)to adapt to nonlinear tracking.The simulation results show that the proposed algorithm has better performance on the multi-target's state estimation and potential estimation than the traditional algorithm of Gaussian mixture probability hypothesis filtering.