Multi-peak FBG reflection spectrum segmentation based on continuous wavelet transformation

Ding, Peng; Huang, Junbin; Tang, Jinsong

doi:10.1016/j.yofte.2019.04.003

Cited by 13 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the complexity increases when faced with the task of detecting multiple sensor peaks, necessitating the application of more intricate methodologies. The wavelet transform stands out as a commonly employed technique for detecting multi-peak responses in FBG sensors (Hu et al, 2016;Ding et al, 2019;. In addition, peak detection methods such as Hilbert transformation (Rochford and Dyer, 1999;Liu et al, 2018), matched filtering technique (Kumar and Sengupta, 2022) or hybrid application of the mentioned methods have also been tried in the literature (Theodosiou et al, 2017;Chen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

Sadik

2023

Preprint

View full text Add to dashboard Cite

This study introduces a novel approach to address the challenge of separating partially and fully overlapped spectra in Fiber Bragg Grating (FBG) sensors. Leveraging the Nonlinear Least-Squares Curve Fitting method with the Levenberg-Marquardt algorithm (LMA), the proposed algorithm successfully detects distinct FBG peaks even in complex spectral superpositions. This investigation utilized two Fiber Bragg Grating (FBG) sensors, with one subjected to varying temperatures and the other maintained at a constant temperature as a reference. The reflection spectra from the FBG sensors were analyzed across 20 distinct temperature values, and the proposed algorithm accurately identified the peak wavelengths of both sensors with precision. Examination of the relative errors in the detected peak wavelengths revealed a maximum percentage error of 0.0025%. This finding underscores the capability of the proposed algorithm to achieve highly accurate peak detection in densely multiplexed FBG sensors, particularly in applications demanding precise measurements across numerous points within a sensor network.

show abstract

Section: Introductionmentioning

confidence: 99%

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

Sadik

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…have been reported [13]. For the multiple-peak detection of FBG based sensors the signal processing techniques such as Hilbert transform [14],segmentation based continuous wavelets transform [15], cross correlation with Hilbert transform [16], invariant moments retrieval method [17], centroid localization algorithm [18] have been demonstrated. In these methods, the drawback is that they fail to detect true peaks if the peaks are very narrow and weak.…”

Section: Introductionmentioning

confidence: 99%

Efficient detection of multiple FBG wavelength peaks using matched filtering technique

Kumar

Sengupta

2022

Opt Quant Electron

View full text Add to dashboard Cite

We propose an efficient technique for FBG peak detection based on matched filtering technique. The matched filtering process is based on resonance point estimation between a standard reference spectral signal and a reflected spectrum of FBG. The desired peak wavelength and corresponding peak intensity are predicted by determining of the cross-correlation between the FBG signal and 3 rd derivative of the reference signal. The peak wavelength and intensity are found from the zero-crossing points of the crosscorrelation function. The Mexican-hat wavelet function is chosen as the reference spectral signal due to its narrow shape. The proposed algorithm can suitably be used for multiple peak detection when several FBGs are cascaded and if the FBG signal is weak and noisy.

show abstract

Wavelength detection technique of overlapping spectra in the serial WDM FBGs by convolutional neural network

Liu

Xia

Xin

et al. 2023

Optical Fiber Technology

View full text Add to dashboard Cite

Multi-peak FBG reflection spectrum segmentation based on continuous wavelet transformation

Cited by 13 publications

References 10 publications

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

Efficient detection of multiple FBG wavelength peaks using matched filtering technique

Wavelength detection technique of overlapping spectra in the serial WDM FBGs by convolutional neural network

Contact Info

Product

Resources

About