A Full Loading-Based MVDR Beamforming Method by Backward Correction of the Steering Vector and Reconstruction of the Covariance Matrix

Abstract

In order to improve the performance of the diagonal loading-based minimum variance distortionless response (MVDR) beamformer, a full loading-based MVDR beamforming method is proposed in this paper. Different from the conventional diagonal loading methods, the proposed method combines the backward correction of the steering vector of the target source and the reconstruction of the covariance matrix. Firstly, based on the linear combination, an appropriate full loading matrix was constructed to correct the steering vector of the target source backward. Secondly, based on the spatial sparsity of the sound sources, an appropriate loading matrix was constructed to further suppress interferences. Thirdly, the spatial response power was utilized to derive a more accurate direction of arrival (DOA) of the target source, which is helpful for obtaining a more accurate steering vector of the target source and a more effective covariance matrix iteratively. The simulation results show that the proposed method can effectively suppress interferences and noise.

Sequenced Steering Vector Estimation for Eigen-Subspace Projection-Based Robust Adaptive Beamformer

“…General RAB design strategies can be executed via two routes; one is to modify the covariance matrix, and the other is to update the SOI SV. The former route includes diagonal loading (DL) [7][8][9][10][11][12][13][14][15][16] and INCM reconstruction [17][18][19][20][21][22][23][24][25][26][27][28]. The latter route includes eigen-subspace projection (ESP) [29][30][31][32][33][34][35][36] and SV estimation (SVE) [37][38][39][40][41][42][43].…”