Robust adaptive beamforming algorithm in the situation of small sample size

Abstract

The most popular robust adaptive beamforming technique is diagonal loading. However, no clear guidelines can be applied to choose the optimal diagonal loading factor, and beamforming techniques based on the uncertainty set of array steering vector still need to specify the bound of the uncertainty set. In this paper, we develop a novel robust adaptive algorithm. Firstly, we modify covariance fitting criteria (CFC) via replacing the conventional sample covariance matrix used in CFC by the general linear combination (GLC) covariance matrix estimates. Then we use the modified CFC to estimate the covariance matrix of array observation data in the standard capon beamforming formulation. The merits of our method we term GLC-CFC include applicability to arbitrary number of snapshots, robustness to the correlated sources and without the requirement of specifying any user-parameters. The excellent performance of our method in small snapshots size is demonstrated via numerical examples and compared with other classical adaptive beamforming algorithms.