Sparse array DOA estimation based on matrix completion

Xingyu Zou; Yibin Rui; Baiyi Shao; Renhong Xie; Peng Li

doi:10.1117/12.3023070

28 March 2024 Sparse array DOA estimation based on matrix completion

Xingyu Zou, Yibin Rui, Baiyi Shao, Renhong Xie, Peng Li

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 1309106 (2024) https://doi.org/10.1117/12.3023070
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingyu Zou, Yibin Rui, Baiyi Shao, Renhong Xie, and Peng Li "Sparse array DOA estimation based on matrix completion", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 1309106 (28 March 2024); https://doi.org/10.1117/12.3023070

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KEYWORDS

Matrices

Chemical elements

Covariance matrices

Signal to noise ratio

Computer simulations

Monte Carlo methods

Mathematical optimization

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