Intrusion signal extraction and recognition for optical fiber perimeter system using improve CFAR and support vector machine

Fa-shuo Yu; Jia-qing Mo; Xiang-xiang Zheng

doi:10.1117/12.2542482

18 December 2019 Intrusion signal extraction and recognition for optical fiber perimeter system using improve CFAR and support vector machine

Fa-shuo Yu, Jia-qing Mo, Xiang-xiang Zheng

Author Affiliations +

Proceedings Volume 11340, AOPC 2019: Optical Fiber Sensors and Communication; 113400L (2019) https://doi.org/10.1117/12.2542482
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

The key technology and main difficulty for optical fiber perimeter system is the extraction and recognition of intrusion signals, vibration signals normally consist of noises, intrusion and disturb signals. Firstly, a new detection method combining constant false alarm rate (CFAR) method and Level Crossing (LC) method was proposed to distinguish the intrusion and no-intrusion signal before recognition. The former can produce adaptive thresholds to eliminate noise and disturb signals according to the background homogeneity, the later can ensure the integrity of the intrusion signal and further reduce disturb signal. Second, multi-feature parameters including traditional timedomain features, wavelet packet energy Shannon entropy and wavelet packet energy, energy proportion, kurtosis, skewness are accurately extracted from the intrusion signal. Finally, use support vector machine (SVM) identify multi-feature vectors of different types of vibration signals. The proposed method was experimented on Sagnac optical fiber pre-warning system. The result show that the method can extract vibration signal effectively form sensing signals, improve the system recognition rate.

Citation Download Citation

Fa-shuo Yu, Jia-qing Mo, and Xiang-xiang Zheng "Intrusion signal extraction and recognition for optical fiber perimeter system using improve CFAR and support vector machine", Proc. SPIE 11340, AOPC 2019: Optical Fiber Sensors and Communication, 113400L (18 December 2019); https://doi.org/10.1117/12.2542482

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KEYWORDS

Signal detection

Detection and tracking algorithms

Optical fibers

Signal processing

Wavelets

Feature extraction

Pattern recognition

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