Sparse array DOA estimation based on matrix completion
Zou Xingyu, Yinbin Rui, Baiyi Shao, Renhong Xie, Li Peng · 2024
In this paper, we propose a modified augmented Lagrange multiplier method to improve the estimation performance of the direction of arrival (DOA) of sparse arrays. We use the duality of the augmented Lagrangian multipliers to optimize the dual solution based on the residual term generated during the iteration process, and the Artificial Fish Swarm Algorithm (AFSA) is used to adaptively update the coefficient of the residual term, so as to improve the accuracy of the original solution of the matrix completion problem. Simulation results show that, this method has better DOA estimation performance compared to the traditional Augmented Lagrangian Method (ALM) and Singular Value Thresholding (SVT), and can be applied to coherent sources.