基于多雷达组网IMM-GMPHDF的机动多目标检测跟踪

高斯混合概率假设密度滤波(GMPHDF)有牢固的理论基础,是解决高斯条件下跟踪强杂波环境中目标数未知的多目标问题的有效方法。但当目标发生机动时,就难以跟踪到目标,因此,在GMPHDF中引入交互多模型(IMM)算法,对继续存在目标的运动模型进行建模,根据计算的模型概率融合各模型滤波器估计得到的继续存在目标概率假设密度,解决了运动模型机动问题。仿真实验表明,IMM-GMPHDF能实时跟踪到强机动超音速多目标,在多雷达组网系统中跟踪强机动超音速多目标精度(OSPA距离均方根误差)能达到70 m,满足了工程使用要求。

Study on Tracking Maneuvering Multi-target Based on IMM-GMPHDF in Multi-radar Networking

Gaussian mixture probability hypothesis density filter (GMPHDF),which is effective method for tracking unknown number of multi-target in srrong clutter environment,has solid theoretical basis. But it hard to track target with GMPHDF when the targets maneuver. To model manuvering target,interacting mutiple model(IMM)in GMPHDF is introduced,modeling motion model of continued target and fusing probability hypothesis density of each model filter based on latest model probability. The simulation results show that it can real-time track strong maneuvering and supersonic multi-target with IMM-GMPHDF,whose tracking precision can reach 70 m in multiradar networking system,which meet the project requirement.