{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:12:21Z","timestamp":1780330341291,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2022YFG0084"],"award-info":[{"award-number":["2022YFG0084"]}]},{"name":"Sichuan Science and Technology Program","award":["2020-MS5-146"],"award-info":[{"award-number":["2020-MS5-146"]}]},{"name":"Key Technology Projects in the Transportation Industry in 2020","award":["2022YFG0084"],"award-info":[{"award-number":["2022YFG0084"]}]},{"name":"Key Technology Projects in the Transportation Industry in 2020","award":["2020-MS5-146"],"award-info":[{"award-number":["2020-MS5-146"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Distributed optical fiber acoustic sensing (DAS) is promising for long-distance intrusion-anomaly detection tasks. However, realistic settings suffer from high-intensity interference noise, compromising the detection performance of DAS systems. To address this issue, we propose STNet, an intrusion detection network based on the Stockwell transform (S-transform), for DAS systems, considering the advantages of the S-transform in terms of noise resistance and ability to detect disturbances. Specifically, the signal detected by a DAS system is divided into space\u2013time data matrices using a sliding window. Subsequently, the S-transform extracts the time-frequency features channel by channel. The extracted features are combined into a multi-channel time-frequency feature matrix and presented to STNet. Finally, a non-maximum suppression algorithm (NMS), suitable for locating intrusions, is used for the post-processing of the detection results. To evaluate the effectiveness of the proposed method, experiments were conducted using a realistic high-speed railway environment with high-intensity noise. The experimental results validated the satisfactory performance of the proposed method. Thus, the proposed method offers an effective solution for achieving high intrusion detection rates and low false alarm rates in complex environments.<\/jats:p>","DOI":"10.3390\/s24051570","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T02:59:39Z","timestamp":1709175579000},"page":"1570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["STNet: A Time-Frequency Analysis-Based Intrusion Detection Network for Distributed Optical Fiber Acoustic Sensing Systems"],"prefix":"10.3390","volume":"24","author":[{"given":"Yiming","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5491-1745","authenticated-orcid":false,"given":"Jianwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhong","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Deng","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maoning","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4755","DOI":"10.1109\/JLT.2019.2919713","article-title":"Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach","volume":"37","author":"Shiloh","year":"2019","journal-title":"J. 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