Robust Adaptive Beamforming with Projection-Based Covariance Matrix Reconstruction Under Colored Noise

Extant adaptive beamforming techniques employ the interference-plus-noise covariance matrix (INCM) to safe-guard the desired signal from suppression. In the presence of colored noise, null offsets may occur, resulting in degradation of beamformer performance. To bolster the beamformer's ro-bustness under colored noise, a novel reconstruction method for the INCM is proposed. This method initially estimates the steering vector of the desired signal by integrating the principal eigenvalue and projection methods. Subsequently, the colored noise covariance matrix is reconstructed utilizing the properties of the blocking matrix. Ultimately, the power of the interference signal is obtained through the projection of the received data, thereby yielding the INCM. The computational complexity of the proposed algorithm stands favorably in comparison to that of other algorithms. Simulation results demonstrate that this method exhibits strong robustness to mismatch errors under colored noise, regardless of whether the SNR is high or low, or whether the number of snapshots is high or low.