A multi-peak detection algorithm for FBG based on WPD-HT

An accurate multi-peak detection algorithm based on wavelet packet decomposition (WPD) denoising and Hilbert transform (HT) is proposed. WPD and wavelet threshold method are used to denoise the high-frequency part of the spectrum. HT and parabola fitting are used to divide the peak area adaptively and calculate the central wavelength. Simulation results show that compared with the other four denoising methods, this denoising algorithm can solve the problem of background noise more effectively. Compared with the Gaussian LM (Levenberg-Marquardt) algorithm, Centroid method, and polynomial fitting method, this method has higher precision. Experimental results of real-time temperature detection show that the stability is better than 0.5 pm at 0 degrees C and the temperature monitoring accuracy is better than 0.352 degrees C in the range of - 20 degrees C~ 40 degrees C, which is the best among the four algorithms. The computational speed is 81.2% higher than the Gauss LM algorithm. In general, this method can be applied to FBG real-time temperature monitoring and demodulation system under the condition of background noise.

Automated summary

The proposed algorithm combining wavelet packet decomposition denoising and Hilbert transform can effectively solve background noise issues, achieving higher accuracy and faster speed than conventional methods. It demonstrates outstanding performance in real-time temperature detection.