The high-resolution underwater azimuth estimation algorithm for function-feature subspaces

In response to the problem of weak azimuth resolution of traditional azimuth estimation methods under limited array apertures, a high-resolution azimuth estimation algorithm based on the theory of function-feature subspace is proposed. The proposed method enhances the subspace decomposition form of the eigenspace algorithm by improving the projection scanning vectors in the eigenspace algorithm and incorporates the functional beamforming algorithm during the calculation process to achieve azimuth estimation in the function-feature subspace based on exponential control. Theoretical analysis and simulation results demonstrate that the algorithm encompasses the conventional eigenspace algorithm and provides improved sidelobe suppression and narrower main lobe width, while maintaining noise resistance performance. Experimental data results indicate that this algorithm surpasses eigenspace and other beamforming methods in terms of resolution capability, making it highly promising for practical applications.