Robust Adaptive Beamforming via MEPS-Based Covariance Matrix Reconstruction and Steering Vector Estimation in Space-Polarization Joint Domain for Mainlobe Interference Suppression

In scenarios where mainlobe interference and steering vector (SV) mismatch coexist, conventional robust adaptive beamforming techniques are prone to main-beam distortion, desired signal self-cancellation, and degradation of output performance. To address this problem, this paper proposes a mainlobe interference suppression method based on robust adaptive beamforming in the space-polarization joint domain. Firstly, the extended form of the Maximum Entropy Power Spectrum (MEPS) for polarization-sensitive arrays is theoretically derived. Owing to the high-resolution capability of MEPS under limited snapshots and multiple closely spaced mainlobe interferences, the Capon power spectrum is replaced by it for covariance matrix reconstruction. Then, a convex optimization problem with newly designed constraints is formulated to estimate the SV of the desired signal, which prevents the estimated SV from biasing toward interference directions. Subsequently, diagonal loading technology is introduced to further strengthen the robustness of the proposed method. Simulations are conducted under three typical SV mismatch scenarios. The results show that the proposed method exhibits stronger robustness under the coexistence of mainlobe interference and SV mismatch, achieving output performance closer to the theoretical optimum.