Abstract

Developing an intrusion event identification scheme, which concurrently possesses high recognition accuracy and high efficiency, has been an intractable task in optical fiber perimeter security systems. To achieve high recognition accuracy, we apply the all-phase filter (APF) bank in frequency division and choose the envelope fluctuation parameter to describe the waveform feature of APFs’ outputs. To achieve high recognition efficiency, we introduce the random forest classifier to recognize intrusion types, which not only alleviates the negative effect arising from occasionality or randomness of intrusions, but also bypasses tedious computation of existing classifiers applied to optical fiber dual Mach-Zehnder Interferometry based perimeter security system. Experimental results demonstrate that the proposed system can distinguish 6 typical patterns (kicking the fence, cutting the fence, waggling the fence, knocking the fence, climbing the fence, and no intrusion) with the average recognition rate of $\text{96.92}\%$ . Moreover, the consumed training time is reduced to about $\text{40}\%$ of the Support Vector Machine. Therefore, the proposed scheme has vast potentials in actual applications.

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