Abstract
For the Sagnac fiber perimeter security system, the recognition rate of intrusion signal is not high. This paper proposes a new endpoint detection algorithm and recognition algorithm to effectively improve the recognition rate of intrusion signals. The short-term logarithmic energy and spectral entropy characteristics are combined to form a new endpoint detection algorithm to improve the accuracy of endpoint detection. The recognition algorithm uses variational mode decomposition to extract the spectral entropy, energy ratio and kurtosis of the eigenmode function. Including the multi-dimensional features of time domain and frequency domain, using the uncertainty to reduce the feature dimension. The support vector machine is selected to realize the intrusion signal recognition. The experimental results show that the proposed recognition algorithm can effectively identify the tapping, walking and stone throwing signals. The correct recognition rate reached 98.0 %.
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