基于最大平均协方差的雷达波束调度算法研究

A Radar Beam Scheduling Algorithm Based on Maximum Average Covariance

针对相控阵雷达在多目标跟踪过程中的波束调度管理问题,以及传统的波束调度问题中仅优化所有目标中误差最小的目标,面对高机动目标会导致一些误差较大的目标不收敛甚至丢失的问题,提出一种基于最大平均协方差的雷达波束调度算法,并结合交互式多模型滤波(IMM)算法和相控阵雷达无惯性采样的优势,实现对多目标的稳定跟踪的波束调度,并探究期望协方差与过程误差协方差(噪声误差)对跟踪过程的影响。仿真实验表明:该算法可以有效实现多个高机动目标的协方差保持在期望范围内以及实现动态收敛,并可以通过调节期望协方差与过程误差协方差来影响跟踪过程的置信度与收敛速度,实现多目标稳定跟踪。

This paper aims at the beam scheduling management problem of phased array radar in the process of multi-target tracking,and in the traditional beam scheduling,only the target with the smallest error among all targets is optimized,which leads to the problem that some targets with larger errors will not converge or even be lost when facing high maneuvering targets.In view of this,a radar beam scheduling algorithm based on maximum average covariance is proposed,which combines the advantages of the Interactive Multi-Model(IMM) filtering algorithm with that of phased array radar without inertial sampling, to realize beam scheduling for stable tracking of multiple targets, and study the effect of expected covariance and process error covariance(noise error) on the tracking process.The simulation experiments show that the algorithm can effectively keep the covariance of multiple high maneuvering targets within the expected range and achieve dynamic convergence,and can affect the confidence and convergence speed of the tracking process by adjusting the expected covariance and process error covariance,so as to achieve stable tracking of multiple targets.