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Acta Optica Sinica, Volume. 44, Issue 1, 0106016(2024)

Performance Enhancement Method of Optical Frequency Domain Reflection Distributed Fiber Sensing Based on Kalman Prediction

Hong Dang1,2, Bin Ma1,3, Chao Gao1,3, Wenlong Zu1,3, Linqi Cheng2,4, Jinna Chen2, Huanhuan Liu2, Kunpeng Feng1,3,5、*, Xuping Zhang5, and Ping Shen2,4
Author Affiliations
  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China
  • 2Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
  • 3Key Laboratory of Non-destructive Testing and Monitoring Technology for Highspeed Transport Facilities, Ministry of Industry and Information Technology, Nanjing 211106, Jiangsu, China
  • 4Pengcheng Laboratory, Shenzhen 518055, Guangdong, China
  • 5Key Laboratory of Intelligent Optical Sensing and Manipulation, Ministry of Education, Nanjing 210023, Jiangsu, China
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    AbstractGet PDF(in Chinese)
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    References (17)
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    Figures & Tables(10)
    Schematic diagram of typical polarization diversity OFDR system

    Fig. 1. Schematic diagram of typical polarization diversity OFDR system

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    Analysis of TLS tuning nonlinearity. (a) Nonlinear phase of coherent detection signal; (b) nonlinear phase of TLS output beam

    Fig. 2. Analysis of TLS tuning nonlinearity. (a) Nonlinear phase of coherent detection signal; (b) nonlinear phase of TLS output beam

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    PoD fluctuations of sensing gauges at different positions induced by frequency sampling error. (a) Probability density function; (b) cumulative distribution function

    Fig. 3. PoD fluctuations of sensing gauges at different positions induced by frequency sampling error. (a) Probability density function; (b) cumulative distribution function

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    Schematic diagram of compensation of accumulated PoD through local search

    Fig. 4. Schematic diagram of compensation of accumulated PoD through local search

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    Distributed sensing results of strain and temperature. (a) Global distributions of strain and temperature; (b) sensing gauge of strain; (c) sensing gauge of temperature

    Fig. 5. Distributed sensing results of strain and temperature. (a) Global distributions of strain and temperature; (b) sensing gauge of strain; (c) sensing gauge of temperature

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    Spectral center shift with strain and temperature

    Fig. 6. Spectral center shift with strain and temperature

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    Estimation of axial PoD along FUT when strain is 5000 με

    Fig. 7. Estimation of axial PoD along FUT when strain is 5000 με

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    Amounts of cross-correlation operations in local search process with and without Kalman prediction

    Fig. 8. Amounts of cross-correlation operations in local search process with and without Kalman prediction

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    Integration of proposed OFDR system. (a) Physical diagram of configuration; (b) results of strain measurement

    Fig. 9. Integration of proposed OFDR system. (a) Physical diagram of configuration; (b) results of strain measurement

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    • Table 1. Pseudo-code for Kalman dynamic prediction of local search location

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      Table 1. Pseudo-code for Kalman dynamic prediction of local search location

      Step No.Instruction
      L01Sampling reference spectrum（ReS）in time domain and Fourier transforming it to spatial domain
      L02Segment spatial domain according to distribution of sensing gauges along FUT［7］
      L03Sampling measurement spectrum（MeaS）in time domain and Fourier transforming it to spatial domain
      L04Initialization of Kalman filter，let ΣPoD=0
      L05for i=1 to N
      L06Shift ith measurement spectrum by ΣPoD（MeaSi）and correlate it in spatial domain with ith reference spectrum（ReSi），obtaining JC2 and sensing result in ith gauge（SRi）
      L07Kalman prediction according to Eqs.（4）-（8）*，obtaining JC1，then initializing J=0
      L08if（JC1>TJC1 and JC2<TJC2）
      L09for j=±1 to ±M （local search loop）**
      L10Shift MeaSi by j positions and correlate it with ReSi，then update JC2 and sensing result（sri，j）
      L11Kalman prediction according to Eqs.（4）-（8），obtaining JC1
      L12if（JC1<TJC1）
      L13Record SRi←sri，j，J←j
      L14Break current local search loop
      L15end
      L16Search j minimize JC2，then record corresponding SRi←sri，j，J←j
      L17end
      L18end
      L19Calculate PoD of MeaSi according to J，then update ΣPoD←ΣPoD+PoD
      L20end
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    Hong Dang, Bin Ma, Chao Gao, Wenlong Zu, Linqi Cheng, Jinna Chen, Huanhuan Liu, Kunpeng Feng, Xuping Zhang, Ping Shen. Performance Enhancement Method of Optical Frequency Domain Reflection Distributed Fiber Sensing Based on Kalman Prediction[J]. Acta Optica Sinica, 2024, 44(1): 0106016

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Jul. 26, 2023

    Accepted: Aug. 16, 2023

    Published Online: Jan. 5, 2024

    The Author Email: Feng Kunpeng (kpfeng@nuaa.edu.cn)

    DOI:10.3788/AOS231316

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology