CN117168515A - High-spatial-resolution OFDR data processing method based on blind source separation - Google Patents
High-spatial-resolution OFDR data processing method based on blind source separation Download PDFInfo
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Abstract
The application discloses a high spatial resolution OFDR data processing method based on blind source separation. The OFDR sensing system of the independent component analysis algorithm provided by the application can improve the spatial resolution of the measurement system, so that the OFDR sensing system has greater advantages and wider application in the high-precision monitoring fields of aviation, machine equipment and the like.
Description
Technical Field
The application belongs to the technical field of optical fiber sensing detection, and particularly relates to a high-spatial-resolution OFDR data processing method based on blind source separation.
Background
The optical fiber in the distributed optical fiber sensing is used as a sensing medium and is also used as a measured transmission medium, and real-time monitoring of the external environment along the length direction of the optical fiber is realized by utilizing the transmission characteristics of the light waves in the optical fiber, including Raman scattering, rayleigh scattering and Brillouin scattering. The distributed optical fiber sensing technology has the advantages of strong electromagnetic interference resistance, relatively simple structure, high spatial resolution, long sensing distance and the like. Based on the above advantages, the technology is gradually applied to more and more fields, such as detection of bridge safety, detection of civil engineering, underground fire alarms of tunnels and the like, survey geology and the like, and plays an important role in social construction. As a representative of the distributed optical fiber sensing system, the Optical Frequency Domain Reflection (OFDR) has the advantages of light weight, small volume, high sensitivity, strong electromagnetic interference resistance, high spatial resolution and the like, and can continuously measure the external physical quantity changes such as strain, vibration, temperature and the like along the optical fiber distance. In recent years, with the development of OFDR technology, application of shape sensing and acoustic sensing has also been realized. The OFDR system has the characteristic of high spatial resolution, and the spatial resolution of the system can reach millimeter magnitude, so that the OFDR system has very important application in the field of high-precision monitoring such as aerospace and the like. However, as the spatial resolution of the measurement increases, the cross-correlation of the reference signal with the test signal may be greatly reduced, resulting in multi-peak and spurious peaks in the cross-correlation results, and incorrect results. Therefore, how to effectively improve the spatial resolution of the OFDR system is a very important research direction.
Disclosure of Invention
Based on the problems, the application provides a high-spatial-resolution OFDR data processing method based on blind source separation, which effectively improves the spatial resolution of an OFDR system on the premise of not changing the system structure and increasing the cost. The technical proposal is that,
a high spatial resolution OFDR data processing method based on blind source separation comprises the following steps:
s1, respectively acquiring signals twice, wherein one signal is a signal which does not contain strain information and is a reference signal; the other signal is a signal containing strain information and is a test signal;
s2, dividing the reference signal and the test signal into N equal parts on a distance domain according to a certain window size C;
s3, using fast Fourier transform for each piece of distance domain information of the reference signal and the test signal;
s4, performing cross-correlation operation on the reference signal after the inverse Fourier transform and the test signal to obtain a one-dimensional cross-correlation result;
s5, repeating the steps S3-S4 to obtain a cross-correlation result of each corresponding position of the optical fiber, and rearranging all the obtained one-dimensional cross-correlation results as a function of the optical fiber distance into a two-dimensional image signal A;
s6, using a simulation graph which generates a plurality of pairs of noiseless two-dimensional images with the same statistical distribution with the cross-correlation result as a training set B;
s7, selecting part of the image blocks in the training set B obtained in the S6 as training sub-image blocks, and carrying out mean value removal and whitening treatment on the training sub-image blocks to obtain noiseless data B;
s8, performing FastICA algorithm processing on the noiseless data b to obtain a mixed matrix W k Then calculate the separation matrix
S9, probability density s of each component i From the following componentsEstimation, wherein W i Is the separation matrix of the i-th component; from s i Determining a maximum likelihood function of the contraction function g (u);
s10, preprocessing the two-dimensional image signal A obtained in the step S5 in the same way as in the step S7 to obtain noisy data a, and performing independent component analysis transformation on the data a through y=Wa to obtain projection y of the two-dimensional image signal A under a separation matrix W;
s11, obtaining denoising estimation by using the maximum likelihood function of the contraction function g (u) in the step S9Inversion transformation by independent component analysis>Obtaining a low noise cross-correlated two-dimensional image estimate +.>
S12, willAnd reconstructing to obtain the spectral offset of each position of the optical fiber, so that a measurement result under high spatial resolution can be obtained.
Preferably, the spectrum offset obtaining method in step S12 is that,
will beAnd decomposing the result to the corresponding position of the optical fiber by taking the optical fiber distance as a function, and obtaining the offset of the spectrum of the corresponding optical fiber position by searching the offset of the main peak.
An OFDR system comprises a first coupler, a second coupler, a Mach-Zehnder interferometer, an acquisition card, a first polarization controller, a second polarization controller and a Fresnel ring; the continuous laser output of the tunable laser source is divided into two parts by the first coupler, 10% of the continuous laser output is incident to an unbalanced Mach-Zehnder interferometer, a trigger signal is provided for the acquisition card, and the rest part of light enters the second coupler; the coupler is divided into two parts, wherein 1% of output is adjusted through a first polarization controller, so that p and s light components have the same power, 99% of the light components enter a sensing optical fiber through a circulator and a second polarization controller to be detected, a Fresnel ring is used for inhibiting the Fresnel reflection at the tail end of the optical fiber, and a Rayleigh scattering signal and 1% of interference signal obtained by combining the laser output from the coupler are decomposed into p and s components through a polarization beam splitter; finally, the light of p and s is collected by the collecting card.
Advantageous effects
1) The OFDR sensing system of the Gaussian filter denoising algorithm can improve the spatial resolution of a measuring system, so that the OFDR sensing system has greater advantages and wider application in the high-precision monitoring fields of aerospace, machine equipment and the like.
2) According to the OFDR sensing system based on the independent component analysis denoising algorithm, through denoising processing of two-dimensional image information, the spatial resolution of the system can be improved, abnormal values of a measurement result can be effectively removed, and the measurement accuracy is improved.
Drawings
FIG. 1 is a process flow diagram of the present application.
Fig. 2 is a schematic diagram of an OFDR system.
Wherein 1-is a tunable laser; 2-is a coupler I; 3-is a coupler II; 4-is a circulator; 5-is Mach-Zehnder interferometer; 6 is a polarization controller I; 7-is a polarization controller II; 8-is a coupler III; 9-is a polarizing beam splitter; 10-is a balance detector; 11-is an acquisition card; 12-sensing optical fibers; 13-phenanthrene ring.
FIG. 3 is a graph of the results of 100. Mu.. Epsilon. Unused this method at 70.1m for a sensing fiber with a spatial resolution of 0.4mm.
FIG. 4 is a graph of the results of this method applied 100. Mu.. Epsilon. At 70.1m to a sensing fiber with a spatial resolution of 0.4mm.
Fig. 5 is a training set of five simulated noiseless cross-correlation images generated by Matlab, wherein the middle white stripes simulate the unstrained positions and the two side stripes simulate the strained positions.
Detailed Description
The techniques are further described below in conjunction with figures 1-5 and the specific embodiments to aid in understanding the present application.
A high spatial resolution OFDR data processing method based on blind source separation comprises the following steps,
s1, respectively acquiring signals twice, wherein one signal is a signal which does not contain strain information and is a reference signal; the other signal is a signal containing strain information and is a test signal.
S2, dividing the reference signal and the test signal into N equal parts on a distance domain according to a certain window size C;
s3, using fast Fourier transform for each piece of distance domain information of the reference signal and the test signal;
s4, performing cross-correlation operation on the reference signal after the inverse Fourier transform and the test signal to obtain a one-dimensional cross-correlation result;
s5, repeating the steps S3-S4 to obtain a cross-correlation result of each corresponding position of the optical fiber, and rearranging all the obtained one-dimensional cross-correlation results as a function of the optical fiber distance into a two-dimensional image signal A;
s6, generating a plurality of pairs of noiseless simulation graphs with the same statistical distribution as the cross-correlation result two-dimensional images by utilizing Matlab, and taking the simulation graphs as a training set B;
s7, selecting part of the image blocks in the training set B obtained in the S6 as training sub-image blocks, and carrying out mean value removal and whitening treatment on the training sub-image blocks to obtain noiseless data B;
s8, performing FastICA algorithm processing on the noiseless data b to obtain a mixed matrix W k Then calculate the separation matrix
S9, probability density s of each component i From the following componentsEstimation, wherein W i Is the separation matrix of the i-th component; from s i Determining the maximum likelihood function of the contraction function g (u), wherein u has no specific meaning, is a variable, and can refer to s or y above;
s10, preprocessing the two-dimensional image signal A obtained in the step S5 in the same way as in the step S7 to obtain noisy data a, and performing independent component analysis transformation on the data a through y=Wa to obtain projection y of the two-dimensional image signal A under a separation matrix W;
s11, obtaining denoising estimation by using the maximum likelihood function of the contraction function g (u) in the step S9Inversion transformation by independent component analysis>Obtaining low-noise cross-correlation two-dimensional image estimationMeter->
S12, processing the image processed in the step S11And reconstructing to obtain the spectral offset of each position of the optical fiber, so that a measurement result under high spatial resolution can be obtained, and the measurement accuracy is improved.
The spectrum shift amount obtaining method in step S12 is that,
reconstructing the two-dimensional image signal of step S11Decomposing the optical fiber distance into corresponding positions of the optical fibers by taking one pixel as a unit, and obtaining the offset of the spectrum of the corresponding optical fiber position by searching the offset of the main peak.
In fig. 5, the two-dimensional image simulation is two bright stripes, representing unstrained position information near the middle position, representing strained position information away from the middle position.
Example 2
FIG. 3 is a graph of the results of a 400. Mu.. Epsilon. Sensor fiber 73.6m without this method, with a spatial resolution of 2mm, and as can be seen to have a number of outliers, the correct strain distribution along the length of the fiber cannot be obtained.
As can be seen from the figure, the optical fiber at 73.6m to 74m is subjected to micro strain of 400 mu epsilon, and without using the method, the sensing performance of the system on strain is affected due to the higher spatial resolution (2 mm) of the system, so that the sensing result of the micro strain of 400 mu epsilon in the sensing interval has a plurality of abnormal values which are larger than 400 mu epsilon, and the strain distribution which is correct along the length of the optical fiber cannot be obtained
FIG. 4 is a graph of the results of 400 [ mu ] epsilon applied to the sensing fiber 73.6m, which eliminates outliers well, improves system resolution, and yields a correct strain distribution result with 2mm spatial resolution.
As can be seen from the figure, the optical fiber at 73.6m to 74m is subjected to a microstrain of 400 mu epsilon, and in the case of using this method, the results obtained in the sensing region are substantially near the 400 mu epsilon value despite the higher spatial resolution (2 mm) of the system, which means that the method eliminates outliers well, so that the system can still obtain a correct strain distribution result at a high spatial resolution.
Example 3
Fig. 2 is a schematic diagram of an OFDR system. An OFDR sensing system based on distance domain compensation, comprising: the continuous laser output of the tunable laser source is divided into two parts by a coupler I2 (10/90 optical coupler), 10% of the continuous laser output is incident to an unbalanced Mach-Zehnder interferometer 5, a trigger signal is provided for an acquisition card 11, and the rest part of light enters a coupler II 3; then the coupler II 3 (1/99 optical coupler) is divided into two parts, wherein 1% of the output is regulated by the polarization controller I6, so that the p light component and the s light component have the same power, 99% of the output enters the sensing optical fiber 12 through the circulator 4 and the polarization controller II 7 to be detected, the Fresnel ring 13 is used for inhibiting the Fresnel reflection at the tail end of the optical fiber, and then the Rayleigh scattering signal and the interference signal obtained by combining the 1% of the laser output from the coupler III 8 (50/50 optical coupler) are decomposed into the p component and the s component through the polarization beam splitter; finally, the light of p and s is collected by the collecting card.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (2)
1. The high-spatial-resolution OFDR data processing method based on blind source separation is characterized by comprising the following steps of:
s1, respectively acquiring signals twice, wherein one signal is a signal which does not contain strain information and is a reference signal; the other signal is a signal containing strain information and is a test signal;
s2, dividing the reference signal and the test signal into N equal parts on a distance domain according to a certain window size C;
s3, using fast Fourier transform for each piece of distance domain information of the reference signal and the test signal;
s4, performing cross-correlation operation on the reference signal after the inverse Fourier transform and the test signal to obtain a one-dimensional cross-correlation result;
s5, repeating the steps S3-S4 to obtain a cross-correlation result of each corresponding position of the optical fiber, and rearranging all the obtained one-dimensional cross-correlation results as a function of the optical fiber distance into a two-dimensional image signal A;
s6, using a simulation graph which generates a plurality of pairs of noiseless two-dimensional images with the same statistical distribution with the cross-correlation result as a training set B;
s7, selecting part of the image blocks in the training set B obtained in the S6 as training sub-image blocks, and carrying out mean value removal and whitening treatment on the training sub-image blocks to obtain noiseless data B;
s8, performing FastICA algorithm processing on the noiseless data b to obtain a mixed matrix W k Then calculate the separation matrix
S9, probability density s of each component i From the following componentsEstimation, wherein W i Is the separation matrix of the i-th component; from s i Determining a maximum likelihood function of the contraction function g (u);
s10, preprocessing the two-dimensional image signal A obtained in the step S5 in the same way as in the step S7 to obtain noisy data a, and performing independent component analysis transformation on the data a through y=Wa to obtain projection y of the two-dimensional image signal A under a separation matrix W;
s11, obtaining denoising estimation by using the maximum likelihood function of the contraction function g (u) in the step S9Inversion transformation by independent component analysis>Obtaining a low noise cross-correlated two-dimensional image estimate +.>
S12, willAnd reconstructing to obtain the spectral offset of each position of the optical fiber, so that a measurement result under high spatial resolution can be obtained.
2. The method for processing high spatial resolution OFDR data based on blind source separation according to claim 1, wherein the spectrum offset obtaining method in step S12 is that,
will beAnd decomposing the result to the corresponding position of the optical fiber by taking the optical fiber distance as a function, and obtaining the offset of the spectrum of the corresponding optical fiber position by searching the offset of the main peak.
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