利用稀疏贝叶斯推理估计干扰导向矢量和功率的稳健自适应波束形成方法

现有稳健自适应波束形成（Robust adaptive beamforming,RAB）方法对快拍数有较高要求，可用快拍数的不足可能会使RAB方法无效。稀疏贝叶斯推理（Sparse Bayesian Inference,SBI）从贝叶斯的角度，通过对信号的稀疏先验假设来利用稀疏信息，在建模稀疏信号方面具有较好的灵活性，可以提高解的稀疏性，即使在样本数较低的情况下也能取得很好的估计效果。本文使用SBI估计干扰信号的导向矢量和功率，提出了一种新颖的基于干扰加噪声协方差矩阵（Interference plus Noise Covariance Matrix,INCM）重建的RAB方法。所提方法利用SBI在建模稀疏信号方面的优越性，通过准确重建出INCM，实现高输出SINR。仿真结果表明，本文提出的方法在比较宽的输入SNR范围内和少量快拍情况下都实现了较好的性能。

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.