雷达与ESM综合多目标检测、跟踪与识别

为在预警监视系统中对多目标的检测、跟踪、识别过程进行统一处理,提出一种基于跳转马尔可夫系统模型高斯混合概率假设密度滤波(jump Markov system model Gaussian mixture probability hypothesis density filtering,JMS-GMPHDF)算法的雷达、电子支援措施（electronic support measures,ESM）综合多目标检测、跟踪与识别方法。该方法首先根据不同类别目标设计各自的多目标多模型高斯混合概率假设密度滤波器,并在各滤波器处理过程中同时对高斯项进行编号;然后,根据目标速度与加速度模型信息进行高斯项综合与类别判决,同时根据ESM测量信息进行型号判决;最后,通过航迹综合管理,形成具有运动状态信息以及类别、型号、航迹编号信息的确定航迹。仿真实验验证了该方法能够有效综合雷达、ESM测量数据,在进行多目标检测、跟踪的同时进行正确的类别、型号判决,并形成确定航迹。

Multi-target detection,tracking and recognition with fusion of radar and ESM sensors

For recognizing the multi-target simultaneously with those targets detected and tracked in modern early warning and surveillance system, based on the jump Markov system model Gaussian mixture probability hypothesis density filtering (JMS-GMPHDF), a method is proposed for multi-target detection, tracking and recognition by fusion of radar and electronic support measures （ESM） sensors. First, the independent multi-target multi-model Gaussian mixture probability hypothesis density filter for each class of targets is designed, and Gaussian terms labels in each filtering process are given. Then, the Gaussian terms are merged and the class is estimated by targets velocity and acceleration model, and the type is estimated by ESM measurement. Finally, by managing tracks, determinate tracks with kinematic states, class, type, and track number are formed. Simulation results suggest that the proposed method can recognize the targets effectively and formulate correct tracks during the detecting and tracking process.