为进一步提升双基站无源协同跟踪系统的目标跟踪精度,提出了一种最小化克拉美罗下界(CRLB)迹的接收站路径优化方法。该方法结合部分可观马尔可夫决策过程(POMDP)构建接收站路径优化模型,并设计了基于CRLB的接收站路径优化代价函数;在满足接收站自身运动约束的前提下,求解最小化目标代价函数的接收站控制指令,最终实现接收站路径的实时优化;考虑到代价函数的高度非线性,为减少计算量,保证优化实时性,在接收站运动速度矢量转向角约束内进行离散化取值,获取接收站路径优化的近似最优解。实验结果表明,相比于现有的主流方法,该文方法能够有效减少接收站路径优化算法的计算量,同时显著提升目标跟踪精度。

In order to improve the target tracking accuracy of the dual-base station passive cooperative tracking system furthermore, a path optimization method of receiving station with minimum the trace of CRLB is proposed. This method combines partial considerable Markov decision process (POMDP) to construct the receiving station path optimization model, and designs the cost function of receiving station path optimization based on CRLB. On the premise of satisfying the motion constraint on the receiving station itself, the control instructions of the receiving station with the minimum target cost function are solved, and the real-time optimization of the receiving station path is finally realized. Due to the high nonlinearity of the cost function, in order to reduce the computation and ensure the real-time performance, this paper discretizes the value of the constraints of the steering angle of the motion velocity vector of the receiving station, so as to obtain the approximate optimal solution for the path optimization of the receiving station. The experimental results show that, compared with the existing mainstream methods, the proposed method can effectively reduce the calculation amount of the path optimization algorithm of the receiving station and significantly improve the target tracking accuracy.

TN953; TN957

国家自然科学基金资助项目(61901151);浙江省自然科学基金资助项目(LQ19F010009)