A robust adaptive beamforming method based on the matrix reconstruction against a large DOA mismatch

A novel adaptive beamforming algorithm against large direction-of-arrival (DOA) mismatch without using optimization toolboxes is proposed. In contrast to previous works, this new beamformer employs two reconstructed matrices, the interference-plus-noise covariance matrix and the desired signal-plus-noise covariance matrix, instead of their real sample covariance matrix, respectively. These reconstructed covariance matrices are used to obtain an orthogonal subspace, which is orthogonal to the interference subspace and contains the desired signal subspace. Without estimating the desired signal steering vector, an optimal weight can finally be solved by rotating this orthogonal subspace based on the output power of the desired signal maximization. This novel beamformer is able to keep a steady and outstanding performance when DOA mismatch has a large uncertainty level. Moreover, this algorithm overcomes the problem of the desired signal self-cancelation at high signal-to-noise ratio (SNR) while maintaining the good performance at low SNR.