A new 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 on the uncertainty set of the presumed steering vector, the optimized solution is that the assumed steering vector can be modified as the weighed sum of the vectors orthogonally projected to the signal subspace and the noise subspace. Using the modified steering vector, the spectral peaks in the actual signal directions are guaranteed. Consequently, the problem of signal self-canceling encountered by the adaptive beamforming due to the steering vector mismatches is eliminated. Simulation and lake trial results show that the proposed method not only has the high resolution capability, but also is robust to a few steering vector errors. Furthermore, the modified MUSIC algorithm outperforms the conventional MUSIC and SSMUSIC methods excellently.