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VMD-based vibrating fiber system intrusion signal recognition

机译:基于VMD的振动光纤系统入侵信号识别

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摘要

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 %.
机译:对于Sagnac光纤外围安全系统,入侵信号的识别率不高。 本文提出了一种新的终点检测算法和识别算法,有效地提高了入侵信号的识别率。 组合短期对数能和光谱熵特性以形成新的终点检测算法,以提高端点检测的准确性。 识别算法使用变分模式分解以提取特征模函数的光谱熵,能量比和峰度。 包括时域和频域的多维特征,使用不确定性来减少特征尺寸。 选择支持向量机以实现入侵信号识别。 实验结果表明,该识别算法可以有效地识别攻丝,步行和石头投掷信号。 正确的识别率达到98.0%。

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