Robust Capon beamforming against large DOA mismatch

In the presence of significant direction-of-arrival (DOA) mismatch, existing robust Capon beamformers based on the uncertainty set of the steering vector require a large size of uncertainty set for providing sufficient robustness against the increased mismatch. Under such circumstance, however, their output signal-to-interference-plus-noise ratios (SINRs) degrade. In this paper, a new robust Capon beamformer is proposed to achieve robustness against large DOA mismatch. The basic idea of the proposed method is to express the estimate of the desired steering vector corresponding to the signal of interest (SOI) as a linear combination of the basis vectors of an orthogonal subspace, then we can easily obtain the estimate of the desired steering vector by rotating this subspace. Different from the uncertainty set based methods, the proposed method does not make any assumptions on the size of the uncertainty set. Thus, compared to the uncertainty set based robust beamformers, the proposed method achieves a higher output SINR performance by preserving its interference-plus-noise suppression abilities in the presence of large DOA mismatch. In addition, computationally efficient online implementation of the proposed method has also been developed. Computer simulations demonstrate the effectiveness and validity of the proposed method. ► A robust Capon beamformer against large DOA mismatch is proposed. ► The proposed method can be easily solved without any specific optimization software. ► The proposed method does not make any assumptions on the size of the uncertainty set. ► The online implementation of the proposed method is also developed.