Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 23; p. 7783
• Main Authors: Duan, Yanliang, Yu, Xinhua, Mei, Lirong, Cao, Weiping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23.11.2021; MDPI
• Subjects: Approximation; Beamforming; blocking matrix; Complexity; Covariance matrix; Eigenvectors; interference-plus-noise covariance matrix reconstruction; Linear programming; Methods; Noise; orthogonality; Projection; Reconstruction; robust adaptive beamforming; Sensors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21237783

Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.