有限快拍条件下的MMV-OMP波达方向估计算法OA

To address the performance degradation of direction of arrival(DOA)estimation under snapshot-limited conditions in underwater acoustic environments,a sparse reconstruction method based on the multiple measurement vector(MMV)model is investigated.An extended MMV-orthogonal matching pursuit(OMP)algorithm is developed by jointly processing multi-snapshot observations and exploiting the joint sparsity of signals to achieve iterative support set refinement,thereby improving DOA estimation accuracy under low signal-to-noise ratio(SNR)conditions.Comparative simulations are conducted under varying SNR levels,and the performance is quantitatively evaluated using root mean square error(RMSE)and detection probability(DP).The results show that,with 10 snapshots and an SNR of-4 dB,the proposed algorithm improves the detection probability by approximately 2.2%and reduces the RMSE by about 1.01° compared with benchmark methods,while maintaining low computational complexity.Experimental validation based on anechoic water tank data demonstrates that the proposed algorithm produces continuous and stable DOA estimation trajectories with well-focused spectral peaks,enabling effective tracking of target direction variations.The results indicate that MMV-OMP achieves high estimation accuracy and strong noise robustness under snapshot-limited conditions,and exhibits promising potential for practical applications.