Spatial Signal Focusing and Noise Suppression for Direction-of-Arrival Estimation in Large-Aperture 2D Arrays under Demanding Conditions

Direction-of-Arrival (DOA) estimation in sensor arrays faces limitations under demanding conditions, including low signal-to-noise ratio, single-snapshot scenarios, coherent sources, and unknown source counts. Conventional beamforming suffers from sidelobe interference, adaptive methods (e.g., MVDR) and subspace algorithms (e.g., MUSIC) degrade with limited snapshots or coherent signals, while sparse-recovery approaches (e.g., L1-SVD) incur high computational complexity for large arrays. In this article, we construct the concept of the optimal spatial filter to solve the DOA estimation problem under demanding conditions by utilizing the sparsity of spatial signals. By utilizing the concept of the optimal spatial filter, we have transformed the DOA estimation problem into a solution problem for the optimal spatial filter. We propose the Spatial Signal Focusing and Noise Suppression (SSFNS) algorithm, which is a novel DOA estimation framework grounded in the theoretical existence of an optimal spatial filter, to solve for the optimal spatial filter and obtain DOA. Through experiments, it was found that the proposed algorithm is suitable for large aperture two-dimensional arrays and experiments have shown that our proposed algorithm performs better than other algorithms in scenarios with few snapshots or even a single snapshot, low signal-to-noise ratio, coherent signals, and unknown signal numbers in two-dimensional large aperture arrays.