稀疏重构类算法在雷达目标参数估计中的应用一直是近年来的热门，但由于稀疏重构类算法的局限性，在进行目标波达方向(direction of arrival，DOA)估计时受到原子间的互相影响，从而使多目标测角精度降低。针对此问题，提出一种基于信号分离迭代思想的松弛子空间追踪算法。首先求出回波信号与归一化后字典矩阵相关性最强的多个原子作为初步估计值，再利用初步估计的角度构建代价函数，反复估计直至代价函数收敛。仿真结果表明，所提算法减小了目标个数和相位差的影响，提高了多目标DOA估计的测角精度，同时相较于传统的松弛算法减少了运算量。
Multi-target DOA estimation method based on improved SP algorithm
The application of sparse reconstruction algorithms in radar target parameter estimation has been a hot topic in recent years.However,due to the limitations of sparse reconstruction algorithms,they are affected by the mutual influence between atoms when estimating the direction of arrival(DOA)of target waves,resulting in a decrease in the accuracy of multi-target angle measurement.To address this issue,a relaxation subspace tracking algorithm based on the idea of signal separation iteration is proposed.Firstly,the multiple atoms with the strongest correlation between the echo signal and the normalized dictionary matrix are calculated as the initial estimated values.Then,the initial estimated angles is used to construct the cost function,and estimate repeatedly until the cost function converges.The simulation results show that the proposed algorithm reduces the influence of the number of targets and phase difference,improves the angle measurement accuracy of multi-target DOA estimation,and reduces the computational complexity compared to traditional relaxation algorithms.
direction of arrival(DOA)estimationsparsereconstructionsubspace pursuitrelaxation algorithm
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