Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming

Abstract--This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) est ima-tion mismatches and data covariance matrix reconstruction problems. In particular, the direction-of-arrival(DoA) of int erfering sources is estimated with available snapshots in which the a ngular sectors of the interfering signals are computed adaptively . Then, we utilize the well-known general linear combination algor ithm to reconstruct the interference-plus-noise covariance (I PNC) matrix using preprocessing-based spatial sampling (PPBSS). We demonstrate that the preprocessing matrix can be replaced b y the sample covariance matrix (SCM) in the shrinkage method. A power spectrum sampling strategy is then devised based on a preprocessing matrix computed with the estimated angular sectors' information. Moreover, the covariance matrix for the signal is formed for the angular sector of the signal-of-int erest (SOI), which allows for calculating an SV for the SOI using the power method. An analysis of the array beampattern in the proposed PPBSS technique is carried out, and a study of the computational cost of competing approaches is conducte d. Simulation results show the proposed method's effectivene ss compared to existing approaches. DAPTIVE beamforming spans across various fields, including wireless communications, radar, sonar, and medical imaging, where it significantly improves performan ce by increasing signal-to-noise ratio (SNR) and mitigating i n-terference [1].