Database-Guided Local Refinement and Truncated Power Estimation for LEO Uplink Anti-Interference Beamforming

Uplink emergency communications for LEO satellites are vulnerable to severe co-channel interference from dense terrestrial networks. This paper proposes a database-aided robust adaptive beamforming framework that treats database angles as coarse priors and compensates for spatial mismatches. The method includes three stages: (1) local spatial refinement via constrained Capon search to accurately locate interferers while protecting the desired signal; (2) truncated adaptive amplitude (TAA) estimation to recover true interference powers and suppress noise artifacts; and (3) reconstruction of the interference-plus-noise covariance matrix (INCM) for MVDR beamforming. By avoiding global angular scanning and improving spatial statistics estimation, the approach achieves near-optimal SINR under limited snapshots and strong interference. Simulations show consistent performance gains over DL-MVDR, ESB, IAA, and existing database-assisted methods across varying SNR, INR, and mismatch conditions, demonstrating strong robustness and practical applicability.