This paper targets a dynamic statistical threshold detection algorithm
of fiber Bragg grating (FBG) spectral peaks at the presence of changing
Signal-to-Noise Ratio (SNR) in an optical fiber of a sensing
application. The proposed post-demodulation SNR-based detection
implements sliding window technique. Its detection threshold is adapted
by the targeted probability of false alarms and by background noise
statistics. The proposed detection algorithm is independent of FBG
spectral peaks shapes and simple enough computationally to implement. It
has been demonstrated and validated using sensor network with a deployed
group of FBG-based sensors, by implementing simplified sliding window
technique. When the adjacent FBG spectral peaks overlap partially, it
provides a high degree of certainty in rejecting false FBG detection.
The algorithm can be used for „spectral windowing“ or precise
measurement of FBG spectral peaks parameters, especially in densely
populated FBG sensor networks. This can lead to a significantly higher
spectral bandwidth utilization in FBG sensing applications.