FBG signal self-adapting restoration method based on compressed sensing

Abstract

The invention relates to a FiberBragg grating(FBG) signal self-adapting restoration method based on compressed sensing, and belongs to a signal restoration technology field of an optical fiber sensing system. The FBG signal self-adapting restoration method comprises steps that step 1: EMD combination mutual information is used for self-adapting denoising processing of spectral signals; step 2, segmented testing of a denoising signal is carried out, and the signal is divided into k segments, and sample databases corresponding to the signals are acquired by calculating Euclidean distances among various segments of signals and samples, and self-adapting dictionaries D corresponding to the signals are acquired by adopting a K-SVD dictionary learning method; step 3, measured signals are used to acquire observation matrixes R and observation signals xi; step 4, the observation signals are reconstructed by adopting an improved regularized orthogonal matching pursuit algorithm to acquire complete reconstructed signals. The FBG signal self-adapting restoration method is advantageous in that problems such as interferences of noises on the signals, targeted dictionary learning, and the signal self-adapting reconstruction are considered, and each part represents the self-adaptability of the algorithm, and can be flexibly used in practical engineering, and then influences caused by manual misoperation are reduced.

Description

technical field [0001] The invention belongs to the technical field of signal processing of an optical fiber sensing system, and relates to an adaptive repair method of FBG signals based on compressed sensing. Background technique [0002] Fiber Bragg grating (Fiber Bragg grating, FBG) sensor, as a passive fiber optic device, is widely used in the detection of bridges, highways, tunnels, aviation and other engineering projects because of its intrinsic safety, anti-electromagnetic interference, and easy networking. In actual engineering, there will be two problems: first, the traditional demodulation method needs to obtain a large amount of data, and there are certain challenges to the realization of efficient and timely collection, transmission and reconstruction of tens of thousands of grating spectra; second During the collection process, data loss at a certain moment or within a certain period of time may be caused by sensor damage, transmission network congestion, or sof...

AI Technical Summary

Problems solved by technology

In actual engineering, there will be two problems: first, the traditional demodulation method needs to obtain a large amount of data, and there are certain challenges to the realization of efficient and timely collection, transmission and reconstruction of tens of thousands of grating spectra; second During the collection process, the data may be lost at a certain moment or within a certain period of time due to sensor damage, transmission network congestion, or software packet loss, which will bring inconvenience to our evaluation and analysis of the project.

However, simply using this training method cannot completely obtain a dictionary that matches the characteristics of the observed signal. Therefore, it is still necessary to design a reasonable sparse representation method to effectively represent the signal.

[0004] There are three main types of reconstruction algorithms for the third step of CS: the first type is convex relaxation algorithm, such as BP algorithm. This type of algorithm has the characteristics of high reconstruction accuracy, but it has high computational complexity and long running time. It is difficult to reconstruct large signals; the second type is joint algorithms, such as Fourier sampling, chain tracking algorithms, etc., which can effectively improve operating efficiency, but the reconstructed signal is not accurate; the third type is greedy algorithms, For example, MP, OMP, ROMP, SAMP, etc. These algorithms effectively compromise the first two types of algorithms, but these algorithms usually need to set certain parameters, such as relying on sparsity, and sparsity is often an uncertain parameter in actual engineering

The SAMP algorithm does not need to rely on sparsity, but it needs to design a reasonable step size, so there is a certain dependence. If the design is not reasonable, the reconstruction accuracy will be unsatisfactory

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