Wideband DOA estimation via low rank and denoising covariance matrix reconstruction
Juan Shi, Lihuan Huang, Weidong Wang, Quanhe Chen, Xuan Shi · 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · 2022
1In wideband array signal processing, most existing direction of arrival (DOA) estimation methods have poor performance with finite snapshots. Aiming at this problem, this paper proposes a novel DOA estimation method for wideband signal sources using denoising covariance matrix reconstruction based on Toeplitz structure presented, followed by the low rank and denoising MUSIC method. Specifically, the wideband signal covariance matrix with finite snapshots is directly modeled as the sum of the covariance matrix of the signal sources and that of the noise according to the low rank recovery theory. Then, a convex model is established by weakening the noise subspace of the data sampling covariance matrix. Afterwords, a low rank and denoising Toeplitz covariance matrix is reconstructed via performing semidefinite programming (SDP) on the optimization target. Finally, the target angles are efficiently estimated by the improved MUSIC method. The numerical simulation results show that the improved wideband DOA (WDOA) estimation method solution outperforms the other two classical DOA estimation methods in the case of small angular separation and low signal to noise ratio(SNR).