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. 2024 Feb 15;24(4):1253.
doi: 10.3390/s24041253.

An Optical Frequency Domain Reflectometer's (OFDR) Performance Improvement via Empirical Mode Decomposition (EMD) and Frequency Filtration for Smart Sensing

Maxim E Belokrylov  1 , Dmitry A Kambur  1   2 , Yuri A Konstantinov  1 , D Claude  1 , Fedor L Barkov  1
Affiliations

An Optical Frequency Domain Reflectometer's (OFDR) Performance Improvement via Empirical Mode Decomposition (EMD) and Frequency Filtration for Smart Sensing

Maxim E Belokrylov et al. Sensors (Basel). .

Abstract

We describe a method for reducing the cost of optical frequency domain reflectometer (OFDR) hardware by replacing two reference channels, including an auxiliary interferometer and a gas cell, with a single channel. To extract useful information, digital signal processing methods were used: digital frequency filtering, as well as empirical mode decomposition. It is shown that the presented method helps to avoid the use of an unnecessary analog-to-digital converter and photodetector, while the OFDR trace is restored by the equal frequency resampling (EFR) algorithm without loss of high resolution and with good measurement repeatability.

Keywords: OFDR; auxiliary interferometer; empirical mode decomposition; gas cell; optical frequency domain reflectometry; optical measurements.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Smart city monitoring concepts: pointwise and distributed ones.
Figure 2
Figure 2
Experimental setup 1.
Figure 3
Figure 3
Recording of the gas cell channel in time.
Figure 4
Figure 4
Experimental setup 2.
Figure 5
Figure 5
Data received from the combined reference channel of setup 2: (a) complete data set; (b) one of the gas cell peaks enlarged.
Figure 6
Figure 6
Data processing scheme: (a) a fragment of an algorithm with empirical modes circled in blue, excluding residual one (b).
Figure 6
Figure 6
Data processing scheme: (a) a fragment of an algorithm with empirical modes circled in blue, excluding residual one (b).
Figure 7
Figure 7
(a) The IMFs (1–3) of the signal. X-axis—normalized amplitude; Y-axis—time (samples). (b) The IMFs (4–7) of the signal. X-axis—normalized amplitude; Y-axis—time (samples). (c) The IMFs (8–11) of the signal. X-axis—normalized amplitude; Y-axis—time (samples). (d) The IMFs (12–14) of the signal. X-axis—normalized amplitude; Y-axis—time (samples).
Figure 7
Figure 7
(a) The IMFs (1–3) of the signal. X-axis—normalized amplitude; Y-axis—time (samples). (b) The IMFs (4–7) of the signal. X-axis—normalized amplitude; Y-axis—time (samples). (c) The IMFs (8–11) of the signal. X-axis—normalized amplitude; Y-axis—time (samples). (d) The IMFs (12–14) of the signal. X-axis—normalized amplitude; Y-axis—time (samples).
Figure 8
Figure 8
Demonstration of the need to use AUX in OFDR setup.
Figure 9
Figure 9
The OFDR traces reconstructed using AUX, but without the gas cell.
Figure 10
Figure 10
The OFDR traces reconstructed using AUX and GC (for empirical mode decomposition).
See this image and copyright information in PMC

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