{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T02:10:36Z","timestamp":1767665436004,"version":"3.41.2"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T00:00:00Z","timestamp":1633564800000},"content-version":"vor","delay-in-days":279,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52071164"],"award-info":[{"award-number":["52071164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster\u2013Shafer (ML\u2010DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence\u2010based decision\u2010making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two\u2010dimensional spectrogram features and one\u2010dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision\u2010making of D\u2010S evidence theory of different times, the decision\u2010making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal\u2010to\u2010noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one\u2010step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML\u2010DS proposed in this paper can be applied in the field of ship radiated noise identification.<\/jats:p>","DOI":"10.1155\/2021\/8901565","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T14:49:06Z","timestamp":1633877346000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Ship Radiated Noise Recognition Technology Based on ML\u2010DS Decision Fusion"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9249-3049","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8733-8252","authenticated-orcid":false,"given":"Chengxi","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2720-5199","authenticated-orcid":false,"given":"Yunan","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4517-5421","authenticated-orcid":false,"given":"Mingliang","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7816-7041","authenticated-orcid":false,"given":"Hanqiong","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1917-2772","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,7]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"9633","article-title":"Marine vessel recognition by acoustic signature","volume":"10","author":"Leal N.","year":"2015","journal-title":"ARPN Journal of Engineering and Applied Sciences"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1121\/10.0000921"},{"key":"e_1_2_9_3_2","first-page":"297","article-title":"Development of underwater acoustic target feature analysis and recognition technology","volume":"34","author":"Fang S.","year":"2019","journal-title":"Bulletin of Chinese Academy of Sciences"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/952798"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1121\/1.3365315"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18040952"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dt.2019.07.020"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/7864213"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"LimT. 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