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基于時/頻域綜合特征提取的分布式光纖入侵監測系統事件識別方法

广东11选5开奖助手Event Discrimination Method for Distributed Optical Fiber Intrusion Sensing System Based on Integrated Time/Frequency Domain Feature Extraction

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摘要

針對分布式光纖入侵監測系統在室外復雜環境下誤報率過高的問題,提出了一種基于時/頻域綜合特征提取的入侵事件識別方法。使用自適應幅值門限信號切分算法找出有效振動信號片段,在此基礎上提取平均片段間隔特征。選取最大能量片段作為主要研究對象,提取片段長度和峰均比特征,并對其進行小波包分解,生成頻域能量分布特征,組成時/頻域復合特征向量,使用高性能的支持向量機多分類算法進行模式識別。實驗結果表明:該方法對行人腳踩、自行車軋過、拍擊圍欄和剪切光纜這4種典型入侵事件的平均識別正確率達到了98.33%,相比于僅提取時域或頻域特征方法的識別正確率均有顯著提高。該方法對光路光功率變化不敏感,能有效提升系統的實用性。

Abstract

To reduce the high false alarm rate of the distributed fiber intrusion monitoring system in outdoor complex environment, this study proposes and demonstrates an intrusion event discrimination method based on integrated time/frequency domain feature extraction. First, a vibration fragment segmentation algorithm based on a self-adaptive amplitude threshold is developed to distinguish the vibrating part. On this basis, the average fragment interval feature is extracted. Next, the vibration fragment with the maximum energy is chosen as the research target, and the length and peak-to-average ratio are extracted in the time domain, whose energy distribution in the frequency domain is calculated according to wavelet packet decomposition and an integrated time/frequency domain feature vector is formed. Finally, one-versus-one support vector machine is used to classify four common intrusion events: footsteps of a passerby, bicycle rolling, knocking on the fence, and cutting of an optical cable. The experimental results show that the proposed method recognizes the abovementioned four common intrusion events with an average accuracy of 98.33%, which is much more accurate than the methods that only extract the time or frequency domain features. Moreover, the proposed method is immune to the optical power variation in light path. Thus, the proposed method is helpful to improve the utility of the system.

補充資料

DOI:

所屬欄目:遙感與傳感器

广东11选5开奖助手基金項目:國家自然科學基金面上項目、湖北省自然科學基金創新群體項目;

收稿日期:2018-12-17

修改稿日期:2019-02-21

網絡出版日期:2019-06-17

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彭寬:華中科技大學光學與電子信息學院, 湖北 武漢 430074武漢飛思靈微電子技術有限公司, 湖北 武漢 430074
馮誠:華中科技大學光學與電子信息學院, 湖北 武漢 430074
王森懋:華中科技大學光學與電子信息學院, 湖北 武漢 430074
艾凡:華中科技大學光學與電子信息學院, 湖北 武漢 430074
李豪:華中科技大學光學與電子信息學院, 湖北 武漢 430074
劉德明:華中科技大學光學與電子信息學院, 湖北 武漢 430074
孫琪真:華中科技大學光學與電子信息學院, 湖北 武漢 430074

聯系人作者:孫琪真(qzsun@hust.edu.cn)

備注:國家自然科學基金面上項目、湖北省自然科學基金創新群體項目;

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引用該論文

Kuan Peng, Cheng Feng, Senmao Wang, Fan Ai, Hao Li, Deming Liu, Qizhen Sun. Event Discrimination Method for Distributed Optical Fiber Intrusion Sensing System Based on Integrated Time/Frequency Domain Feature Extraction[J]. Acta Optica Sinica, 2019, 39(6): 0628002

广东11选5开奖助手 彭寬, 馮誠, 王森懋, 艾凡, 李豪, 劉德明, 孫琪真. 基于時/頻域綜合特征提取的分布式光纖入侵監測系統事件識別方法[J]. 光學學報, 2019, 39(6): 0628002

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