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Frequency-estimation-based signal-processing algorithm for white-light optical fiber Fabry-Perot interferometers.

Fabin Shen1, Anbo Wang

  • 1Center for Photonics Technology, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA. fashen1@vt.edu

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A new algorithm estimates frequency from optical fiber sensor spectrograms. This method enables precise, high-dynamic-range measurements, even with noise and varying light sources.

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Area of Science:

  • Optoelectronics
  • Fiber Optic Sensors
  • Signal Processing

Background:

  • Single-mode optical fiber Fabry-Perot interferometric sensors are crucial for various measurement applications.
  • Existing signal processing methods often require spectrogram normalization and struggle with large cavity lengths or noisy conditions.

Purpose of the Study:

  • To introduce a novel signal-processing algorithm for enhanced performance of optical fiber Fabry-Perot sensors.
  • To enable absolute measurements with high resolution and a large dynamic range.

Main Methods:

  • A frequency-estimation algorithm is applied to the spectrogram of optical fiber Fabry-Perot interferometric sensors.
  • The method utilizes linear regression of the instantaneous phase of an analytical signal, derived from a bandpass-filtered spectrogram.
  • The algorithm does not require spectrogram normalization to the light source spectrum.

Main Results:

  • The developed method successfully performs absolute measurements with high resolution and a large dynamic range.
  • Experimental results demonstrate the algorithm's effectiveness for sensors with relatively large cavity lengths.
  • The approach is directly extendable to multiplexed sensor systems.

Conclusions:

  • The novel frequency-estimation algorithm offers a robust and versatile solution for optical fiber sensor signal processing.
  • This method overcomes limitations of previous techniques, showing tolerance to background noise and source spectrum variations.
  • The algorithm facilitates advanced sensing applications requiring precise and reliable measurements.