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First, a fast [Formula: see text]-sparse representation of the array covariance vector model based on the Hermitian Toeplitz structure of array covariance is established to reduce computational complexity in data dimension and variable number. Then, the estimation error upper bound problem is investigated, and a neural network-aided coefficient selection method is developed. The direction of arrival estimation problem is solved through spectral peak search. Finally, the algorithm is extended to the case of off-grid error. The algorithm\u2019s advantages in accuracy, calculation speed and robustness is verified by the simulations.<\/jats:p>","DOI":"10.1177\/01423312211049067","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T06:35:50Z","timestamp":1633588550000},"page":"1649-1655","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Neural network-aided sparse convex optimization algorithm for fast DOA estimation"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3363-3379","authenticated-orcid":false,"given":"Jingyu","family":"Cong","sequence":"first","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering, Hainan University, 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