欺骗干扰下机动目标跟踪联合优化算法

Joint Optimization Algorithm for Maneuvering Target Tracking in Deception Jamming

目标常采取机动加欺骗干扰策略以规避对方雷达系统的锁定。针对同时存在目标机动和距离欺骗的场景,提出了一种目标跟踪联合优化算法。首先,将目标机动和距离欺骗分别建模为动态方程和量测方程中的未知扰动;然后,基于期望最大化(EM)提出了一种状态估计和机动与欺骗参数辨识联合优化算法。为了降低计算量,在期望步(E步)基于URTS平滑器获得状态估计,并在最大化步(M步)解析推导出待辨识参数的更新公式。最后,仿真验证结果表明,该算法与传统交互式多模型(IMM)方法相比具有更高的估计及辨识精度。

The target often adopts the strategy of maneuvering and deception jamming to evade the lock of the opposite radar system.Aimed at the scene with the maneuvering target and the distance deception at the same time,ajoint optimization algorithm for maneuvering target tracking is proposed.Firstly,the target maneuvering and the distance deception are regarded as unknown perturbation in the dynamic equation and the measure equation.Then,ajoint optimization algorithm for the state estimation,the maneuvering parameter and the deception parameter identification is proposed based on the expectation-maximation（EM）.To reduce the calculating amount,the state estimate is obtained based on the unscented Rauch-Tung-Striebel（URTS）smoother in the E-step,and the updating expression of the parameter to be identified is analytically derived in the M-step.Finally,the simulation result shows that the algorithm has higher estimation and identification precision than the traditional interacting multiple model（IMM）method.