2022
DOI: 10.1016/j.measurement.2022.111411
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Valley-positioning-assisted discrete cross-correlation algorithm for fast cavity length interrogation of fiber-optic Fabry–Perot sensors

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Cited by 8 publications
(2 citation statements)
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“…Based on the spectrum pattern of an ideal FP sensor, a simulated spectrum S v λ is constructed. Then, using the software cross-correlation algorithm, the correlation number C between the real spectrum and the virtual spectrum obtained is expressed as [34] C…”
Section: Testing and Discussionmentioning
confidence: 99%
“…Based on the spectrum pattern of an ideal FP sensor, a simulated spectrum S v λ is constructed. Then, using the software cross-correlation algorithm, the correlation number C between the real spectrum and the virtual spectrum obtained is expressed as [34] C…”
Section: Testing and Discussionmentioning
confidence: 99%
“…The main problem in determining the main peak is the extraction of the envelope of a series of peaks in the cross-correlation curve, which may introduce additional errors affecting the correct determination of the main peak. Kang et al 21 proposed a valley-positioning-assisted discrete cross-correlation algorithm, which can greatly increase the cavity length extraction rate by significantly reducing the computation amount. The only problem is that the discretization process makes the demodulation performances greatly depend on the valley positioning precision.…”
Section: Introductionmentioning
confidence: 99%