Efficient Robust Adaptive Beamforming Based on Spatial Sampling with Virtual Sensors

TL;DR

Addresses robustness challenges in adaptive beamforming under steering-vector and geometry mismatches at high SNR. Proposes Low-Complexity Spatial Sampling Process (LCSSP) with virtual sensors and a higher-dimensional projection to sample the interference spectrum while avoiding explicit IPNC reconstruction. Key contributions include a projection-based orthogonal complement, an efficient IPNC estimator, and a complexity reduction to $\mathcal{O}(M^2L)$. Empirical results demonstrate near-optimal SINR and improved robustness compared to several IPNC-based baselines across SNR, INR, and snapshot variations, highlighting practical impact for wireless, radar, and sonar systems.

Abstract

Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (IPNC) matrix reconstruction can experience serious performance degradation in the presence of look direction and array geometry mismatches, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we present a RAB technique to address covariance matrix reconstruction problems. The proposed method involves IPNC matrix reconstruction using a low-complexity spatial sampling process (LCSSP) and employs a virtual received array vector. In particular, we devise a power spectrum sampling strategy based on a projection matrix computed in a higher dimension. A key feature of the proposed LCSSP technique is to avoid reconstruction of the IPNC matrix by integrating over the angular sector of the interference-plus-noise region. Simulation results are shown and discussed to verify the effectiveness of the proposed LCSSP method against existing approaches.

Figures (6)

• Figure 1: Virtual and physical array configuration
• Figure 2: Comparison of the normalized beampatterns
• Figure 3: Output SINR versus Input SNR
• Figure 4: Deviation from optimal SINR versus SNR
• Figure 5: Output SINR versus Number of Snapshots