DOA Estimation via Adaptive Weighted Truncated Nuclear Norm
Zhen Tian, Qile Zhu, Shun Fang, Qi Wu, Shiqian Wu · 2023
Aiming at the large error of traditional direction-of-arrival (DOA) estimation algorithm caused by the low signal-to-ratio and limited sampling. This paper proposes a DOA estimation method based on adaptive weighted truncation nuclear norm. First, a new model which combines γ norm and adaptive weighted nuclear norm is proposed to accurately reconstruct the received signal covariance matrix into a low-rank covariance matrix and a sparse covariance matrix. Next, the generalized alternating direction method of multipliers (GADMM) with an adaptive stopping criterion is used to solve this convex optimization. Finally, based on the reconstructed low-rank noise-free covariance matrix, a multiple signal classification (MUSIC) algorithm is used to achieve DOA estimation. The simulation results are provided to verify the effectiveness and superiority of the proposed method.