基于运动模式集精细差异特征估计的真假弹道目标联合跟踪与辨识方法

Joint Tracking and Recognition Method for Ballistic Targets and False Targets Based on Fine Difference Feature Estimation of Motion Pattern Set

针对对抗条件下弹道目标和有源多假目标跟踪及辨识难的问题,基于稳健交互多模型(Robust Interacting Multiple Model,RIMM)策略,提出真假弹道目标的联合跟踪与辨识方法。该方法基于推导的真假目标运动模式集以及模式间的精细差异特征设计交互多模型(Interacting Multiple Model,IMM)策略,以扩展卡尔曼滤波(Extended Kalman Filter,EKF)为子滤波器,并引入概率调整因子与时变因子,实时更新概率转移矩阵,有效放大运动模式集的精细差异特征,不仅能实现对真假目标的稳定跟踪,提高跟踪精度,同时也能实时在线辨识真假目标,实现跟踪辨识一体化。仿真结果表明,该方法的跟踪效果比传统单模型EKF算法和经典的IMM+EKF算法更好,能实时跟踪并辨识出真假目标,有利于提高雷达资源调度的效率。

Aiming at the difficulty of tracking and recognizing ballistic targets and active multi-false targets in the presence of countermeasures,a joint tracking and recognition method for ballistic targets and false targets based on the robust interacting multiple model(RIMM)strategy is proposed.This method develops the interacting multiple model(IMM)strategy based on the deduced true target and false target motion pattern set and the fine difference features within the set,using the extended Kalman filter(EKF)as sub filters.Additionally,this method introduces probability adjustment factors and time-varying factors into the IMM strategy to update the probability transition matrix in real time and amplify the fine feature difference of the motion pattern set effectively,which not only achieves stable tracking of ballistic targets and false targets,improves the tracking accuracy,but also identifies them online in real time,achieving integrated tracking and identification.Simulation results show that the proposed method has better performance than traditional single model EKF algorithm and classical IMM+EKF algorithm,and it can track and recognize ballistic targets and false targets in real time,which is conducive to improving the efficiency of radar resource scheduling.