{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:09Z","timestamp":1740202029104,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>There are mainly two factors, multipath effect and strong background noise, affecting the performance of underwater weak acoustic signal detection. In this paper, to improve the performance, we propose a joint detection approach for underwater weak acoustic signal by combining the approaches of Passive Time Reversal (PTR) and Stochastic Resonance (SR). By calculating the input and output signal-to-noise ratios (SNR) theoretically, it's found that the proposed PTR-SR approach could improve the SNR of received signal, which is obtained by utilizing the multipath propagation channel and background noise simultaneously. Further, we propose a strategy to properly setting the free amplitude parameter Asrto optimize the SNR gain. Based on the Neyman-Pearson criterion, simulation results also highlight the performance of the proposed joint detection approach over single PTR approach and SR approach, especially in the circumstance of low SNR.<\/jats:p>","DOI":"10.3233\/978-1-61499-927-0-572","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:26:53Z","timestamp":1740133613000},"source":"Crossref","is-referenced-by-count":0,"title":["The Performance of Weak Underwater Acoustic Signal Detection Based on Passive Time Reversal and Stochastic Resonance"],"prefix":"10.3233","author":[{"family":"Liu Lei","sequence":"additional","affiliation":[]},{"family":"Shen Xiaohong","sequence":"additional","affiliation":[]},{"family":"Ma Shilei","sequence":"additional","affiliation":[]},{"family":"Zhang Zhichen","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IV"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:00:15Z","timestamp":1740135615000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-926-3&spage=572&doi=10.3233\/978-1-61499-927-0-572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-927-0-572","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}