Robust adaptive beamforming based on virtual sensors using low-complexity spatial sampling

AbstractHighlights •Design a robust adaptive beamforming method for uniform linear arrays based on a virtual sensor.•A novel approach to reconstruct the IPNC matrix using a low-complexity spatial sampling process (LCSSP).•The power spectrum sampling is realized by a proposed projection matrix orthogonal to the signal subspace that retains the interference-plus-noise in a higher dimension.•A new robust adaptive beamforming algorithm is developed. AbstractThe performance of robust adaptive beamforming (RAB) based on interference-plus-noise covariance (IPNC) matrix reconstruction can be degraded seriously in the presence of random mismatches (look direction and array geometry), particularly when the input signal-to-noise ratio (SNR) is high. In this work, we present a RAB technique to address covariance matrix reconstruction problems. The proposed RAB technique involves IPNC matrix reconstruction using a low-complexity spatial sampling process (LCSSP) and employs a virtual received array vector. In particular, the power spectrum sampling is realized by a proposed projection matrix in a higher dimension. The essence of the proposed technique is to avoid reconstruction of the IPNC matrix by integrating over the angular sector of the interference-plus-noise region. Simulation results are presented to verify the effectiveness of the proposed RAB approach.