Robust Adaptive Beamforming Based on Low Complexity Interference Plus Noise Covariance Matrix Reconstruction

The algorithms based on interference-plus-noise covariance matrix (INCM) reconstruction are regarded as a burgeoning robust adaptive beamforming (RAB) technique in recent years. However, the majority of INCM algorithms prioritize performance in various error scenarios while neglecting the computational complexity of the methods. Consequently, this paper addresses both the robustness and computational efficiency of the INCM algorithm. First, the Capon algorithm is used to estimate the steering vector (SV) and the robust Capon beamforming (RCB) algorithm is used to correct the SV, which improves the anti-error ability of the algorithm. Then, the eigenvalue decomposition is used to obtain the interference and noise power, and the proposed algorithm simplifies the interference power estimation of INCM reconstruction by using the largest eigenvalue, which effectively reduces the computational complexity of the algorithm. Finally, the simulation results show that the proposed INCM algorithm has excellent resistance to different types of errors, taking into account both computational complexity and performance.