SNR-based dynamic statistical threshold detection of FBG spectral peaks

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.