Subarray Adaptive Beamforming Algorithm with Shrinkage and Diagonal Loading under Limited Snapshots

In this paper, a novel subarray-level adaptive beamforming algorithm is proposed to address performance degradation under low snapshot conditions and strong noise environments. The method constructs a subarray signal model and employs Gram matrix-based processing, diagonal loading of the covariance matrix, and shrinkage techniques to improve estimation robustness. Simulation results verify that the proposed algorithm achieves superior output signal-to-interference-plus-noise ratio (SINR), stronger interference suppression, and more effective beampattern control compared to conventional approaches. The algorithm maintains robust performance in challenging scenarios including low SNR, direction-of-arrival (DOA) mismatch, and mainlobe interference. Additionally, it demonstrates strong adaptability to various array configurations, making it a promising solution for practical applications in array signal processing under complex interference and uncertainty.