Acta Optica Sinica, Volume. 35, Issue 10, 1006002(2015)
Ensemble Empirical Mode Decomposition Based Event Classification Method for the Fiber-Optic Intrusion Monitoring System
A pattern recognition method based on ensemble empirical mode decomposition (EEMD) is proposed for the non-stationary features of output signal in the fiber-optic intrusion monitoring system. The system based on the principle of Mach-Zehnder interferometer and four single-mode optical fibers in the cable are utilized to build up the distributed crosstalk sensor, by which the real-time detection of abnormal events can be realized. The vibration signals are decomposed into a series of intrinsic mode functions (IMF) using the EEMD algorithm with self-adaptability. According to the characteristics of the various vibration signal intensities, a method using the EEMD energy entropy to eliminate the disturbance of non-intrusion events is proposed. Double support vector machine is built to identify the intrusion type. The experimental results illustrate that this method can evidently get rid of the non-intrusion disturbance and effectively discern different intrusion events such as fence-climbing, cableknocking and other signals. The correct recognition rate in average is greater than 92%. What′s more, the alarm rate is increased and the false alarm rate is reduced in the system.
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Jiang Lihui, Gai Jingyan, Wang Weibo, Xiong Xinglong, Liang Sheng, Sheng Xinzhi. Ensemble Empirical Mode Decomposition Based Event Classification Method for the Fiber-Optic Intrusion Monitoring System[J]. Acta Optica Sinica, 2015, 35(10): 1006002
Category: Fiber Optics and Optical Communications
Received: Apr. 22, 2015
Accepted: --
Published Online: Oct. 8, 2015
The Author Email: Jingyan Gai (jy_gai@163.con)