Robust Adaptive Beamforming via Modified Variable Loading with Subsampling Preprocessing

For robust adaptive beamforming (RAB), the variable loading (VL) technique can provide a better trade-off between robustness and adaptivity than diagonal loading (DL). Despite its importance, few research efforts have explored the loading factor for VL to ensure robustness in various environments. Moreover, the performance of VL is restricted by the sample covariance matrix in snapshot deficiency situations. This paper proposes a modified variable loading (VL) method for robust adaptive beamforming, considering imprecise steering vector effects and finite sample size impairments. First, a novel subsampling method is used to construct the calibrated covariance matrix to improve the robustness of the VL in sample-starving scenarios. Then, a parameter-free method for the VL factor is proposed to further enhance the insensitivity to the steering vector mismatches of the antenna array. Simulation results verify the effectiveness and robustness of the proposed method as compared to the traditional VL and other widely used robust techniques.

Automated summary

This paper proposes a modified variable loading (VL) method for robust adaptive beamforming, which improves the robustness of VL by using a novel subsampling method to construct the calibrated covariance matrix and enhances the insensitivity to steering vector mismatches of the antenna array with a parameter-free approach.