Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming
This work provides an incremental improvement for signal processing applications like radar or communications by enhancing beamforming robustness against estimation errors.
The paper tackles robust adaptive beamforming by addressing steering vector mismatches and covariance matrix reconstruction, using preprocessing-based spatial sampling to estimate interference directions and reconstruct matrices, with simulation results demonstrating effectiveness compared to existing methods.
This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) estimation mismatches and data covariance matrix reconstruction problems. In particular, the direction-of-arrival(DoA) of interfering sources is estimated with available snapshots in which the angular sectors of the interfering signals are computed adaptively. Then, we utilize the well-known general linear combination algorithm to reconstruct the interference-plus-noise covariance (IPNC) matrix using preprocessing-based spatial sampling (PPBSS). We demonstrate that the preprocessing matrix can be replaced by 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-interest (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 conducted. Simulation results show the proposed method's effectiveness compared to existing approaches.