Robust Polarization-Domain Adaptive Anti-Jamming via Forgetting-Factor Covariance Estimation and Adaptive Diagonal Loading

To address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance estimation and adaptive diagonal loading. The forgetting-factor recursion assigns larger weights to recent jammer-plus-noise snapshots to track nonstationary interference, while the adaptive loading coefficient is jointly controlled by sample deficiency and covariance condition-number degradation to improve inversion stability. Unlike many robust adaptive beamforming methods that require steering-vector uncertainty sets, mismatch distributions, or subspace information, the proposed method relies only on secondary data and a small set of scalar design parameters. Simulation results based on a synthetic dual-polarized array model show that the proposed method achieves competitive output SINR, effective jammer suppression, and improved robustness to moderate DOA and polarization mismatch under limited-snapshot and time-varying interference conditions. Complexity analysis indicates that the proposed method has the same dominant computational order as standard covariance-based MVDR beamforming, apart from condition-number evaluation. The present validation is simulation-based, and further verification using measured polarimetric radar data, realistic propagation models, or hardware experiments is still required.