A Multi-peak Adaptive Accurate Peak-finding Method for Distributed FBG Sensor Networks

Abstract

The invention relates to a multi-peak self-adaption accurate peak searching method for a distributed FBG (Fiber Bragg Grating) sensing network and belongs to the technical field of signal processing of optical fiber sensing systems. The method comprises the following steps: smoothening a spectral signal by using a five-point sliding mean filtering method to eliminate the influence of signal noise on peak searching accuracy; processing the smoothened spectral signal by utilizing Hilbert conversion to obtain an initial peak positioning point of a multi-peak spectral signal; performing Gabor filtering on the smoothened signal to obtain a division point of a left sideband of a spectrum, and symmetrically obtaining a right division point of a spectrum peak by virtue of the initial peak positioning point; performing peak value area division on a multi-peak spectrum by taking the left and right division points of the spectrum as a boundary; integrating the divided spectrum peak by taking an initial positioning peak value point as the center to obtain the areas of a left half peak and a right half peak, and judging the offset situation of the spectrum peak by comparing the sizes of the left half peak and the right half peak; performing accurate peak searching on each spectrum peak by using an index correction Gauss fitting algorithm to obtain a multi-peak accurate peak value point by virtue of the peak shape of the spectrum peak. The method can be used for self-adaption peak value area division of the multi-peak spectral signal of the distributed sensing network and high-accurate peak value positioning.

Description

technical field [0001] The invention belongs to the technical field of signal processing of an optical fiber sensor system, and relates to a multi-peak self-adaptive and accurate peak-finding method for a distributed FBG sensor network. Background technique [0002] Fiber Bragg grating (FBG) sensor, as a kind of fiber optic passive device, has the advantages of small size, anti-electromagnetic interference, corrosion resistance, high temperature resistance, high sensitivity, and easy serial multiplexing to form a distributed sensor network. Widely used in civil engineering, aerospace, petrochemical and medical engineering and other fields. The FBG sensor indirectly obtains the change of the measured parameter by detecting the shift of the central wavelength of the reflection spectrum, and the central wavelength of the reflection spectrum corresponds to its peak position. Therefore, a high-precision peak-finding algorithm is very important to improve the measurement accuracy ...

AI Technical Summary

Problems solved by technology

The direct peak-finding algorithm and the half-peak detection algorithm have low computational complexity and short response time, but their anti-noise performance is poor, so they are not suitable for FBG spectral peak-finding in complex engineering environments; Monte Carlo algorithm and probability statistics algorithm have poor linearity, Peak-finding accuracy is limited; Gaussian fitting algorithm and polynomial fitting algorithm have high peak-finding accuracy, but they have strict requirements on spectral type; although genetic algorithm can improve peak-finding accuracy, it takes a long training time to determine the parameters. It is not suitable for real-time calculation; the ant colony algorithm has a large calculation cost and slow solution speed; the three-point peak-finding algorithm has greatly improved the peak-finding accuracy compared with the traditional algorithm, but it does not fully consider the influence of spectral asymmetry on the demodulation accuracy ; The peak-finding method based on the Steger image algorithm refers to the Steger algorithm that extracts the peak value of the gray-scale curve of the image, and combines the super-Gaussian model to realize the peak-finding of the asymmetric peak type, but the selection of the model parameters has a great influence on the peak-finding accuracy, making the algorithm App is restricted

[0004] The above peak-finding algorithms all aim to improve the peak-finding accuracy of a single peak, but they do not consider the peak-finding problem of multiple peaks in the reflection spectrum of the distributed sensor network, and the fitting method with high peak-finding accuracy cannot be used for The spectrum of multiple peaks is fitted, so this method can only be applied to the peak finding of a single peak

At present, the multi-peak peak-finding methods for FBG spectrum are based on the premise of knowing the original central wavelength of FBG, and the peak-finding operation is performed on the single peak in the intercepted specific spectral range. not applicable

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