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China","award":["2023BSHEDZZ174"],"award-info":[{"award-number":["2023BSHEDZZ174"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Remote passive sonar detection with low-frequency band spectral lines has attracted much attention, while complex low-frequency non-Gaussian impulsive noisy environments would strongly affect the detection performance. This is a challenging problem in weak signal detection, especially for the high false alarm rate caused by heavy-tailed impulsive noise. In this paper, a novel matched stochastic resonance (MSR)-based weak signal detection model is established, and two MSR-based detectors named MSR-PED and MSR-PSNR are proposed based on a theoretical analysis of the MSR output response. Comprehensive detection performance analyses in both Gasussian and non-Gaussian impulsive noise conditions are presented, which revealed the superior performance of our proposed detector under non-Gasussian impulsive noise. Numerical analysis and application verification have revealed the superior detection performance with the proposed MSR-PSNR detector compared with energy-based detection methods, which can break through the high false alarm rate problem caused by heavy-tailed impulsive noise. For a typical non-Gasussian impulsive noise assumption with \u03b1=1.5, the proposed MSR-PED and MSR-PSNR can achieve approximately 16 dB and 22 dB improvements, respectively, in the detection performance compared to the classical PED method. For stronger, non-Gaussian impulsive noise conditions corresponding to \u03b1=1, the improvement in detection performance can be more significant. Our proposed MSR-PSNR methods can overcome the challenging problem of a high false alarm rate caused by heavy-tailed impulsive noise. This work can lay a solid foundation for breaking through the challenges of underwater passive sonar detection under non-Gaussian impulsive background noise, and can provide important guidance for future research work.<\/jats:p>","DOI":"10.3390\/s24092943","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T14:26:11Z","timestamp":1715005571000},"page":"2943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Matched Stochastic Resonance Enhanced Underwater Passive Sonar Detection under Non-Gaussian Impulsive Background Noise"],"prefix":"10.3390","volume":"24","author":[{"given":"Haitao","family":"Dong","sequence":"first","affiliation":[{"name":"Xi\u2019an Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xidian University, Xi\u2019an 710071, China"},{"name":"Shaanxi Union Research Center of University and Enterprise for Intelligent Spectrum Sensing and Information Fusion, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilei","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Suo","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xidian University, Xi\u2019an 710071, China"},{"name":"Shaanxi Union Research Center of University and Enterprise for Intelligent Spectrum Sensing and Information Fusion, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"key":"ref_1","first-page":"214839","article-title":"Advanced applications for underwater acoustic modeling","volume":"2012","author":"Etter","year":"2012","journal-title":"Adv. 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