SNR-Based Denoising Dynamic Statistical Threshold Detection of FBG Spectral Peaks

This paper targets a Denoising Dynamic Statistical Threshold (DDST) detection algorithm to detect the “presence” of Fiber Bragg Grating (FBG) spectral peaks in noise with changing Signal-to-Noise Ratio (SNR) in a sensing channel. Computing the DDST is based on statistical parameters of the background noise. The DDST is determined by adjusting it using the SNR via determining the targeted probability of false alarms (
p
_FA
). The proposed algorithm incorporates the effect of background noise fluctuations, nonlinear signal attenuation of a Single-Mode Fiber (SMF), as well as influence of the short-term interference. The implemented sliding wavelength window technique in conjunction with the FBG spectral peaks power scaling allow automatic adjusting of
p
_FA
and the DDST. During the possible FBGs resonant wavelengths overlap resulting from approaching/colliding spectral power responses of FBGs, the proposed algorithm also improves the detection robustness and resolving of these overlaps. The DDST marginally takes into account spectral shapes of FBGs resonant wavelength peaks. Advantageously, DDST wavelength resolving is independent of FBG spectral peaks shapes. Our DDST algorithm is also simple to implement. Measurements done by two Optical Spectral Analyzers (OSAs) confirmed significant improvements in the background noise reduction (i.e. signal denoising), noisy FBG spectral peaks shapes smoothing and SNR, improved adjacent FBG spectral peaks detectability and resolving. Our experiments also confirmed usability of the DDST algorithm under severe network conditions (with low reflected FBG power below
$-$
75 dB and low
SNR

$< $
4 dB resulting a standard deviation of
$\sigma >$
7 dB in the background noise fluctuations) with resolution of 3.43 pm.