Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 11
• Main Authors: Gao, Yuxiang, Cai, Qixin
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 08.02.2022; Hindawi Limited
• Subjects: 20th century; Artificial intelligence; Empirical analysis; False alarms; Feature extraction; Feature recognition; Fiber optics; Internet of Things; Light; Optical fibers; Security systems; Sensors; Signal classification; Wavelet analysis; United States > US; China
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/4552388

To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.