Robust Adaptive Beamforming for Estimating Interference Steering Vectors and Power Using Sparse Bayesian Inference

‍ ‍Existing robust adaptive beamforming （RAB） methods require a large number of snapshots， and a lack of available snapshots could render the RAB methods ineffective. Sparse Bayesian inference （SBI） utilizes sparse information by making sparse prior assumptions about signals from a Bayesian perspective. It has excellent flexibility in modeling sparse signals， which can increase the sparsity of the solution， and it can obtain good estimation results even with a small sample size. In this paper， we propose a novel RAB method based on interference plus noise covariance matrix （INCM） reconstruction using SBI to estimate the steering vector and power of interfering signals. The proposed method takes advantage of the superiority of SBI in modeling sparse signals to achieve high output SINR by reconstructing an accurate INCM. Simulation results show that the proposed method achieves better performance in a wide input SNR range and a small number of snapshot.