Robust adaptive beamforming via subspace-based diagonal loading in planar FDA-MIMO radar for main lobe interference suppression

FDA-MIMO radar provides a powerful framework for joint range-angle beamforming and offers superior main lobe interference suppression. However, its effectiveness is limited by the system’s sensitivity to steering vector mismatches, which can cause substantial SINR degradation and main lobe distortion. Diagonal loading (DL) is commonly employed to enhance robustness, but conventional methods often rely on fixed or heuristic loading levels, limiting adaptability and potentially introducing signal distortion under strong interference. To this end, this paper proposes a robust adaptive subspace-based diagonal loading (ASDL) beamforming method for planar FDA-MIMO radar. The beamformer is constrained to a mismatch-resilient subspace constructed from neighboring steering vectors to capture target location uncertainty. A closed-form solution is derived by minimizing a quadratic cost under a distortionless constraint, with the DL factor adaptively computed via Capon spectral estimation. Furthermore, the subspace dimension is automatically optimized based on the eigenvalue distribution of the sample covariance matrix, enabling a robust trade-off between mismatch tolerance and main lobe fidelity. Simulation results show that ASDL consistently delivers superior SINR performance and main lobe interference mitigation, outperforming conventional robust beamformers under various mismatch conditions.