一种基于幅度相位信息辅助的多假设跟踪算法

Multiple Hypothesis Tracking Algorithm Based on Amplitude-Phase Measurement Information Assistance

传统多目标跟踪算法在距离拖引(Range Gate Pull Off,RGPO)干扰场景中,由于无法有效区分真实目标和干扰,将出现目标航迹起始错误、跟踪误差增大、跟踪中断等现象,严重影响跟踪性能。针对这一问题,基于雷达量测的幅度和相位特征提出一种信息辅助多假设跟踪(Information Assisted Multiple Hypothesis Tracking,IA-MHT)算法。IA-MHT算法利用了幅度、相位信息区分目标真实量测和虚假量测,并基于判别结果修正了全局联合假设概率和航迹得分的计算步骤,从而提高算法的抗杂波和抗干扰性能。在RGPO干扰场景中的仿真结果表明,IA-MHT算法能够有效区分干扰量测,获得优于传统MHT算法的数据关联性能。

In range gate pull off(RGPO)jamming scenarios,conventional multiple target tracking algorithm can not effectively distinguish the RGPO deception from real target measurements,resulting in track mis-initialization,increased tracking error,and tracking discontinuity.This paper proposes an information-assisted multiple hypothesis tracking(IA-MHT)algorithm to solve the above problems.The IA-MHT algorithm utilizes amplitude and phase information of radar measurements to distinguish between true and false measurements of targets.Furthermore,the algorithm adjusts the calculation method of global association hypothesis probabilities and track scores,which improves the tracking performance under clutter and RGPO interferences.Simulation results show that the IA-MHT algorithm can effectively distinguish the RGPO deception measurements and obtain superior data association performance compared to the conventional MHT algorithm.