Robust Adaptive Beamforming with SSMUSIC Performance Optimization in the Presence of Steering Vector Errors

A novel subspace projection approach was proposed to improve the robustness of adaptive beamforming and direction finding algorithms. The cost function of the signal subspace scaled multiple signal classification (SSMUSIC) is minimized in the uncertainty set of the signal steering vector, the optimal solution to the optimization problem is that the assumed steering vector can be modified as the weighed sum of the vectors orthogonally projected onto the signal subspace and the noise subspace. Using the estimated steering vector with small error to the true steering vector, the spectral peaks in the actual signal directions are guaranteed. Consequently, the problem of signal self-canceling encountered by adaptive beamforming due to steering vector mismatches is eliminated. Simulation and lake trial results show that the proposed method not only possesses high resolution performance, but also is robust to a few steering vector errors. Furthermore, the modified MUSIC algorithm outperforms the conventional MUSIC and SSMUSIC methods excellently.