{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:59:29Z","timestamp":1760241569938,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T00:00:00Z","timestamp":1528070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funndation of State Commission of Science Technology of China","award":["17-H863-05-2T-002-028-02"],"award-info":[{"award-number":["17-H863-05-2T-002-028-02"]}]},{"name":"Foundation of Key Laboratory of Underwater Acoustic Countermeasure","award":["kmb5494"],"award-info":[{"award-number":["kmb5494"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill    K \u2264 (  M 4  \u2212 2  M 3  + 7  M 2  \u2212 6 M ) \/ 8    . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability    O (  M 4  )    , where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.<\/jats:p>","DOI":"10.3390\/s18061815","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T12:14:30Z","timestamp":1528114470000},"page":"1815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector"],"prefix":"10.3390","volume":"18","author":[{"given":"Yangyu","family":"Fan","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6870-5061","authenticated-orcid":false,"given":"Jianshu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Rui","family":"Du","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Guoyun","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.sigpro.2016.03.019","article-title":"Fourth-order cumulants-based sparse representation approach for DOA estimation in MIMO radar with unknown mutual coupling","volume":"128","author":"Liu","year":"2016","journal-title":"Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TSP.2015.2504349","article-title":"CPHD-DOA tracking of multiple extended sonar targets in impulsive environments","volume":"64","author":"Saucan","year":"2016","journal-title":"IEEE Trans. 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