An approach to robust beamforming in the presence of mutual coupling for uniform linear arrays

This paper develops a robust adaptive beamforming algorithm for uniform linear arrays (ULA) in the presence of unknown mutual coupling. In our algorithm, we model the mutual coupling matrix (MCM) as a banded symmetric Toeplitz matrix, which is based on the fact that the mutual coupling between two sensor elements is inversely related to their distance and is negligible when they are spaced far enough. Taking advantage of this specific signal model, a subspace method is used to estimate the mutual coupling coefficients (MCCs) of the ULA that yield a closed-form solution and the MCM estimate can be also obtained. Then, the MCM estimate and the received data are employ to reconstruct an interference-plus-noise matrix. Finally, a robust adaptive beamformer is established using the Capon principle and the reconstructed interference-plus-noise covariance matrix. Simulation results indicate that the proposed beamformer outperforms the existing beamformers and can achieve almost optimal performance across a wide range of signal-to-noise (SNR) ratio or snapshot number.