{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:34:07Z","timestamp":1760142847807,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a new enhanced coprime array for direction of arrival (DOA) estimation. Coprime arrays are capable of estimating the DOA using coprime properties and outperforming uniform linear arrays. However, the associated algorithms are not directly applicable for estimating the DOA of coherent sources. To overcome this limitation, we propose an enhanced coprime array in this paper. By increasing the number of array sensors in the coprime array, it is feasible to enlarge the aperture of the array and these additional array sensors can be utilized to achieve spatial smoothing, thus enabling estimation of the DOA for coherent sources. Additionally, applying the spatial smoothing technique to the signal subspace, instead of the conventional spatial smoothing method, can further improve the ability to reduce noise interference and enhance the overall estimation result. Finally, DOA estimation is accomplished using the MUSIC algorithm. The simulation results demonstrate improved performance compared to traditional algorithms, confirming its feasibility.<\/jats:p>","DOI":"10.3390\/s24010260","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T03:33:33Z","timestamp":1704166413000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced Coprime Array Structure and DOA Estimation Algorithm for Coherent Sources"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiaolei","family":"Han","sequence":"first","affiliation":[{"name":"Institute of IoT Engineering, Shanghai Business School, Shanghai 201400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Electronic Information College, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.sigpro.2013.10.009","article-title":"Robust adaptive beamforming based on interference covariance matrix sparse reconstruction","volume":"96","author":"Gu","year":"2014","journal-title":"Signal Process."},{"key":"ref_2","unstructured":"Johnson, D.H., and Dudgeon, D.E. 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