{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T21:47:17Z","timestamp":1780350437838,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T00:00:00Z","timestamp":1640304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671394"],"award-info":[{"award-number":["61671394"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Fundamental Research Funds for the Central Universities","award":["20720170044"],"award-info":[{"award-number":["20720170044"]}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin","doi-asserted-by":"publisher","award":["16JCQNJC01100"],"award-info":[{"award-number":["16JCQNJC01100"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.<\/jats:p>","DOI":"10.3390\/s22010112","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7317-8908","authenticated-orcid":false,"given":"Hamada","family":"Esmaiel","sequence":"first","affiliation":[{"name":"Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 316005, China"},{"name":"Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4279-0501","authenticated-orcid":false,"given":"Dongri","family":"Xie","sequence":"additional","affiliation":[{"name":"China Electronics Technology Avionics Co., Ltd., Chengdu 610100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyad A. H.","family":"Qasem","sequence":"additional","affiliation":[{"name":"Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 316005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8249-1197","authenticated-orcid":false,"given":"Haixin","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 316005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300383, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xie, D., Esmaiel, H., Sun, H., Qi, J., and Qasem, Z.A. 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