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Intrusion signal recognition based on optoelectronic reservoir computing in optical fiber perimeter systems
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Paper Abstract

In order to reduce the false alarm rates of optical fiber perimeter systems, this paper proposes a signal recognition method based on optoelectronic reservoir computing (RC) to identify pedestrian intrusion signals from various vibration signals acting on the sensing fiber. The optoelectronic reservoir consists of a single nonlinear node and an optoelectronic feedback loop. The nonlinearity of the reservoir is provided by a Mach-Zehnder intensity modulator. The walking signals were acquired in the laboratory through a distributed optical fiber sensing system based on an in-line Sagnac interferometer. The input data of the reservoir is a random combination of walking signals and the output signals of the sensing system under no interferences. The training input data contain three walking signals at different times. The testing input data contain one walking signal according to the most common case. The average identification rate (IR) for the testing data of 10 different walking signals is as high as 97.3%. The highest IR is 99.3%. The simulation results show that the proposed recognition method of intrusion signals of optical fiber perimeter systems is feasible and effective. RC, as an improved training algorithm of recurrent neural networks, has no need of a large number of samples in the training process and seeking the signal features for the signal recognition task. Therefore, the proposed intrusion signal recognition method based on optoelectronic RC is fast in recognition speed and low at cost.

Paper Details

Date Published: 12 December 2018
PDF: 6 pages
Proc. SPIE 10849, Fiber Optic Sensing and Optical Communication, 108491G (12 December 2018); doi: 10.1117/12.2505709
Show Author Affiliations
Ningning Wang, Shanghai Univ. (China)
Nian Fang, Shanghai Univ. (China)
Lutang Wang, Shanghai Univ. (China)


Published in SPIE Proceedings Vol. 10849:
Fiber Optic Sensing and Optical Communication
Jie Zhang; Songnian Fu; Qunbi Zhuge; Ming Tang; Tuan Guo, Editor(s)

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