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Detection and recognition of distributed optical fiber intrusion signal
  • Abstract

    Distributed acoustic sensing (DAS) technology can detect acoustic or vibration signals with high sensitivity and wide dynamic range by receiving the phase information from coherent Rayleigh scattered light. Linear quantization is used to measure high fidelity restoration of the signals. With the increasing demand of practical applications, the optical fiber intrusion detection field has put forward higher requirements for event location and identification, which is manifested as the accurate classification of intrusion events. Therefore, the combination of distributed acoustic sensing and pattern recognition (PR) technology is a hot research topic at present. This is beneficial to promote the application and development of distributed optical fiber sensing technology. The research progress of the pattern recognition technology applied to distributed optical fiber intrusion detection in recent years is summarized in this paper, which can be used for feature extraction and classification algorithm research progress. In this paper, several feature extraction methods for realizing intrusion event signal recognition and their feature selection difficulties in different application situations are reviewed. Meanwhile, the advantages and disadvantages of specific event recognition algorithm are analyzed and summarized.

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  • References

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  • About this Article

    DOI: 10.12086/oee.2021.200254
    Cite this Article
    Zhang Yongkang, Shang Ying, Wang Chen, Zhao Wen′an, Li Chang, Cao Bing, Wang Chang. Detection and recognition of distributed optical fiber intrusion signal. Opto-Electronic Engineering 48, 200254 (2021). DOI: 10.12086/oee.2021.200254
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    Article History
    • Received Date July 09, 2020
    • Revised Date November 19, 2020
    • Published Date March 14, 2021
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    Corresponding author: Wang Chang, ch_wangs@163.com

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    Detection and recognition of distributed optical fiber intrusion signal
    • Figure  1

      DAS system structure

    • Figure  2

      Intrusion signal recognition process

    • Figure  3

      Signal representing a climb during torrential rain as detected. (a) Time domain representations; (b) LC vs. block number[12]

    • Figure  4

      Original signals of five events. (a) Cutting; (b) Waggling; (c) Climbing; (d) Knocking; (e) No intrusion[18]

    • Figure  5

      Segment zero-crossing rates of five events. (a) Cutting; (b) Waggling; (c) Climbing; (d) Knocking; (e) No intrusion[18]

    • Figure  6

      FFT feature extraction flow chart

    • Figure  7

      MFCC feature extraction flow chart

    • Figure  8

      STFT time-frequency diagrams of two kinds of window functions for processing four intrusion events. (a), (c), (e), (g) Time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Hanning window; (b), (d), (f), (h) time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Kaiser window[27]

    • Figure  9

      Fence invasive signals and their IMF components through EMD. (a) EMD of climbing; (b) EMD of knocking; (c) EMD of waggling; (d) EMD of cutting[29]

    • Figure  10

      Signals and their kurtosis eigenvectors of four cases. (a) Climbing signal; (b) Knocking signal; (c) Eigenvectors of climbing; (d) Eigenvectors of knocking; (e) Waggling signal; (f) Cutting signal; (g) Eigenvectors of waggling; (h) Eigenvectors of cutting

    • Figure  11

      Multi-scale decomposition tree. (a) Wavelet decomposition; (b) Wavelet packet decomposition

    • Figure  12

      WE distribution for three typical events[38]

    • Figure  13

      WPE distribution for three typical events[38]

    • Figure  14

      (a) Calculated signal of vehicle passing; (b) Experimentally measured signal of vehicle passing[40]

    • Figure  15

      DBSCAN core and outlier points[41]

    • Figure  16

      Directed acyclic graph of RVM[49]

    • Figure  17

      Feature distribution of three events[40]

    • Figure  18

      Three-layer BP neural network structure

    • Figure  19

      Typical structure of CNN

    • Figure  20

      The effect of spectral subtraction on the vibration signal. (a) The time-domain waveform of the knocking signal after noise reduction; (b) The spectrogram of the knocking signal after noise reduction[55]

    • Figure  21

      The optimized network structure (the red cube denotes convolution operation and the blue cube denotes pooling operation)[56]

    • Figure  22

      Confusion matrix of five events' classification[56]

    • Figure  23

      GAN flow chart

    • Figure  24

      Accuracy and loss of testing datasets at different training algorithms[61]

    • Figure  25

      Cyclic unit structure of LSTM network

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    • Figure  25
    Detection and recognition of distributed optical fiber intrusion signal
    • TimeResearchersFeature extractionClassification algorithmRecognition rate/%
      1IEEE, 2009Qi, et al.FFT+PSDPCA+SVM88.9
      2IEEE, 2010Mahmoud, et al.LCANN
      3APS, 2014Wu, et alSSABP>90
      4ACPC, 2015Cao, et alFFTSVM92.62
      5JLT, 2015Wu, et alWDBP89.19
      6JLT, 2015Liu, et alEMDRBF85.75
      7Sensors, 2015Sun, et alMFERVM+GPU97.8
      8JLT, 2016Tejedor, et alSTFTGMM>55
      9PS, 2017Wu, et alWPDANN94.4
      10ISOP, 2017Aktas.M, et al.STFT2-D CNN>93
      11ICOFS, 2018Shiloh, et al.RGBGAN94
      12JLT, 2019Wei, et alCFARSCN94.67
      13JLT, 2019Wu, et alWPD1-D CNN+SVM96.59
      14OE, 2019Wang, et alRGBDPN+GPU97
      15MOTL, 2020Chen, et alSTE+ZCR+MFCCALSTM94.3
      16OE, 2020Li, et alSTWConvLSTM85.6
    • Table  1

      The development of DAS pattern recognition technology number

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