Efficient detection of multiple FBG wavelength peaks using matched filtering technique

Abstract

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.

Opto-Electronics Review

“…Similarly, the weight 𝑤 𝑖 used in (10) is updated according to the expression given in (10). After training and peak matching of the generated signal and the discriminated signal, the loss functions of discriminator model signal and of generator model signal are calculated using (11), and using (12), the desired peak wavelength as 𝜆 𝐵 is determined.…”

“…To effectively measure the FBG peak, the spectral data is collected, and some suitable peak detection algorithm is generally applied through a computer device. Several peak detection techniques have been developed, e.g., polynomial curve fitting [8], direct method [9], centroid detection method [10], a non-linear Gaussian method [11], etc., for a single FBG peak, and matched filtering technique [12], Hilbert transforms [13], cross-correlation and Hilbert transform [14], self-adaptive [15], invariant moment retrieval [16], etc., for a multiple FBG peak detection. However, these techniques are not dynamic and have a slow time response.…”

Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing

“…To mitigate the impact of the background noise on the quality of the optical signals in telecommunications and sensing systems, hardware pre-processing using real-time wavelength filtering has been typically used [8][9][10]. Software approaches based on verification or post-detection algorithms can be leveraged to detect signals in a complex or fluctuating background noise environment [11,12].…”

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

“…To overcome this limitation, researchers have explored alternative techniques such as spectrometers coupled with advanced signal processing algorithms (Bodendorfer et al, 2009). These advancements enable not only efficient and dynamic peak detection but also facilitate real-time monitoring of environmental changes (Kumar and Sengupta, 2022).…”