Robust Adaptive Beamforming Algorithm Based on Improved Generalized Linear Combination

Conventional adaptive beamforming algorithms often suffer from significant performance degradation when steering vector mismatches and covariance matrix estimation errors occur. To address this problem, this paper proposes an adaptive robust beamforming algorithm based on an improved generalized linear combination (GLC) framework. The proposed method first applies singular spectrum analysis to the received signals to suppress noise components. A diagonal loading coefficient function related to the received signal snapshots is then constructed, and a generalized diagonally loaded covariance matrix is formed using the denoised data. Finally, by exploiting spatial integration and subspace projection within a predefined angular uncertainty set, the actual direction of arrival of the desired signal is accurately estimated, and the steering vector is corrected accordingly. Simulation results demonstrate that, compared with traditional SMI, LSMI, GLC and improved GLC algorithms, the proposed method achieves a 3–5 dB higher output signal-to-interference-plus-noise ratio (SINR) across the entire input signal-to-noise ratio (SNR) range under steering vector mismatch, and reaches an output SINR close to the optimal level with only 100 snapshots, exhibiting excellent robustness against steering vector mismatch and limited snapshot conditions.