{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T15:26:34Z","timestamp":1775834794801,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Russian Science Foundation","award":["22-29-01577"],"award-info":[{"award-number":["22-29-01577"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The purpose of this work is to increase the security of the perimeter of an area from unauthorized intrusions by creating an improved algorithm for classifying acoustic impacts recorded with a sensor system based on a phase-sensitive optical time reflectometer (phi-OTDR). The algorithm includes machine learning, so a dataset consisting of two classes was assembled. The dataset consists of two classes. The first class is the data of the steps, and the second class is other non-stepping influences (engine noise, a passing car, a passing cyclist, etc.). As an intrusion signal, a human walking signal is analyzed and recorded in frames of 5 s, which passed the threshold condition. Since, in most cases, the intruder moves on foot to overcome the perimeter, the analysis of the acoustic effects generated during the step will increase the efficiency of the perimeter detection tools. When walking quietly, step signals can be quite weak, and background signals can contain high energy and visually resemble the signals you are looking for. Therefore, an algorithm was created that processes space\u2013time diagrams developed in real time, which are grayscale images. At the same time, during the processing of one image, two more images are calculated, which are the result of processing the denoised autoencoder and the created mathematical model of the adaptive correlation. Then, the three obtained images are fed to the input of the created three-channel neural network classifier, which includes convolutional layers for the automatic extraction of spatial features. The probability of correctly detecting steps is 98.3% and that of background actions is 97.93%.<\/jats:p>","DOI":"10.3390\/s23146402","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T08:40:06Z","timestamp":1689324006000},"page":"6402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multichannel Classifier for Recognizing Acoustic Impacts Recorded with a phi-OTDR"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4954-5127","authenticated-orcid":false,"given":"Ivan Alekseevich","family":"Barantsov","sequence":"first","affiliation":[{"name":"Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey Borisovich","family":"Pnev","sequence":"additional","affiliation":[{"name":"Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kirill Igorevich","family":"Koshelev","sequence":"additional","affiliation":[{"name":"Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egor Olegovich","family":"Garin","sequence":"additional","affiliation":[{"name":"Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nickolai Olegovich","family":"Pozhar","sequence":"additional","affiliation":[{"name":"Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman Igorevich","family":"Khan","sequence":"additional","affiliation":[{"name":"Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012016","DOI":"10.1088\/1742-6596\/584\/1\/012016","article-title":"Mathematical analysis of marine pipeline leakage monitoring system based on coherent OTDR with improved sensor length and sampling frequency","volume":"584","author":"Pnev","year":"2015","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_2","unstructured":"Svelto, C., Pniov, A., Zhirnov, A., Nesterov, E., Stepanov, K., Karassik, V., and Laporta, P. (2019, January 27\u201329). Online monitoring of gas & oil pipeline by distributed optical fiber sensors. Proceedings of the Offshore Mediterranean Conference and Exhibition, Ravenna, Italy."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Merlo, S., Malcovati, P., Norgia, M., Pesatori, A., Svelto, C., Pniov, A., Zhirnov, A., Nesterov, E., and Karassik, V. (2017, January 21\u201323). Runways ground monitoring system by phase-sensitive optical-fiber OTDR. Proceedings of the 2017 IEEE International Workshop on Metrology for Aero-Space (Metro-AeroSpace), Padua, Italy.","DOI":"10.1109\/MetroAeroSpace.2017.7999629"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16428","DOI":"10.1109\/JSEN.2021.3081459","article-title":"Principle and Application State of Fully Distributed Fiber Optic Vibration Detection Technology Based on \u03a6-OTDR: A Review","volume":"21","author":"Marie","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lu, B., Ye, Q., and Cai, H. (2020). Recent progress in distributed fiber acoustic sensing with \u03a6-OTDR. Sensors, 20.","DOI":"10.3390\/s20226594"},{"key":"ref_6","first-page":"805","article-title":"A novel intrusion signal processing method for phase-sensitive optical time-domain reflectometry (\u03a6-OTDR)","volume":"Volume 9157","author":"Wu","year":"2014","journal-title":"Proceedings of the 23RD International Conference on Optical Fibre Sensors"},{"key":"ref_7","first-page":"5720695","article-title":"Research and software design of an \u03a6-OTDR-based optical fiber vibration recognition algorithm","volume":"2020","author":"Fouda","year":"2020","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1109\/JLT.2013.2273553","article-title":"Enhancement of SNR and Spatial Resolution in \u03c6-OTDR System by Using Two-Dimensional Edge Detection Method","volume":"31","author":"Zhu","year":"2013","journal-title":"J. Light. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"143473","DOI":"10.1109\/ACCESS.2021.3121767","article-title":"A Recognition Method for Multi-Radial-Distance Event of \u03a6-OTDR System Based on CNN","volume":"9","author":"Shi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wen, H., Peng, Z., Jian, J., Wang, M., Liu, H., Mao, Z.-H., Ohodnicki, P., and Chen, K.P. (2018, January 26\u201329). Artificial intelligent pattern recognition for optical fiber distributed acoustic sensing systems based on phase-OTDR. Proceedings of the Asia Communications and Photonics Conference, Hangzhou, China.","DOI":"10.1109\/ACP.2018.8595809"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1177\/1475921720930649","article-title":"A surveillance system for urban buried pipeline subject to third-party threats based on fiber optic sensing and convolutional neural network","volume":"20","author":"Li","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1364\/AO.444811","article-title":"Machine learning methods for identification and classification of events in \u03d5-OTDR systems: A review","volume":"61","author":"Kandamali","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhirnov, A.A., Chesnokov, G.Y., Stepanov, K.V., Gritsenko, T.V., Khan, R.I., Koshelev, K.I., Chernutsky, A.O., Svelto, C., Pnev, A.B., and Valba, O.V. (2023). Fiber-Optic Telecommunication Network Wells Monitoring by Phase-Sensitive Optical Time-Domain Reflectometer with Disturbance Recognition. Sensors, 23.","DOI":"10.3390\/s23104978"},{"key":"ref_14","unstructured":"Salem, H., Negm, K.R., Shams, M.Y., and Elzeki, O.M. (2021). Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments, Springer International Publishing."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103414","DOI":"10.1016\/j.infrared.2020.103414","article-title":"Multi-branch long short-time memory convolution neural network for event identification in fiber-optic distributed disturbance sensor based on \u03c6-OTDR","volume":"109","author":"Wang","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1002\/mop.32025","article-title":"Disturbance pattern recognition based on an ALSTM in a long-distance \u03c6-OTDR sensing system","volume":"62","author":"Chen","year":"2020","journal-title":"Microw. Opt. Technol. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5951","DOI":"10.1364\/AO.458736","article-title":"Temporal convolution network with a dual attention mechanism for \u03c6-OTDR event classification","volume":"61","author":"Tian","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Barantsov, I.A., Pnev, A.B., Koshelev, K.I., Tynchenko, V.S., Nelyub, V.A., and Borodulin, A.S. (2023). Classification of Acoustic Influences Registered with Phase-Sensitive OTDR Using Pattern Recognition Methods. Sensors, 23.","DOI":"10.3390\/s23020582"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yu, J., Yang, Y., Zhang, H., Sun, H., Zhang, Z., Xia, Z., Zhu, J., Dai, M., and Wen, H. (2022). Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules. Micromachines, 13.","DOI":"10.3390\/mi13020332"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/TASLP.2017.2672401","article-title":"Multichannel signal processing with deep neural networks for automatic speech recognition","volume":"25","author":"Sainath","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_21","unstructured":"Juan, C.J. (2005). Distributed Fiber Optic Intrusion Sensor System for Monitoring Long Perimeters. [Ph. D. Thesis, Texas A&M University]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1070\/QE2010v040n10ABEH014389","article-title":"Model of a fibreoptic phase-sensitive reflectometer and its comparison with the experiment","volume":"40","author":"Tosoni","year":"2010","journal-title":"Quantum Electron."},{"key":"ref_23","unstructured":"Chollet, F. (2021). Deep Learning with Python, Simon and Schuster."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1109\/JLT.2021.3138268","article-title":"Optical fiber fault detection and localization in a noisy OTDR trace based on denoising convolutional autoencoder and bidirectional long short-term memory","volume":"40","author":"Abdelli","year":"2021","journal-title":"J. Light. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"26745","DOI":"10.1364\/OE.433690","article-title":"Measurement accuracy enhancement with multi-event detection using the deep learning approach in Raman distributed temperature sensors","volume":"29","author":"Datta","year":"2021","journal-title":"Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_27","unstructured":"Krizhevsky, A., and Hinton, G. (2009). Learning Multiple Layers of Features from Tiny Images, University of Toronto."},{"key":"ref_28","unstructured":"Bieder, F., Sandk\u00fchler, R., and Cattin, P.C. (2021). Comparison of methods generalizing max-and average-pooling. arXiv."},{"key":"ref_29","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Stepanov, K.V., Zhirnov, A.A., Koshelev, K.I., Chernutsky, A.O., Khan, R.I., and Pnev, A.B. (2021). Sensitivity Improvement of Phi-OTDR by Fiber Cable Coils. Sensors, 21.","DOI":"10.3390\/s21217077"},{"key":"ref_31","first-page":"228","article-title":"The proportion for splitting data into training and test set for the bootstrap in classification problems","volume":"12","author":"Vrigazova","year":"2021","journal-title":"Bus. Syst. Res. Int. J. Soc. Adv. Innov. Res. Econ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:04Z","timestamp":1760127124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":31,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146402"],"URL":"https:\/\/doi.org\/10.3390\/s23146402","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,14]]}}}