Distributed OFDR sensing signal demodulation method and system based on local ridge line tracking

Abstract

The application discloses a kind of distributed OFDR sensing signal demodulation method and system based on local ridge line tracking, it is related to optical fiber sensing technical field, to solve the problem that traditional cross-correlation algorithm is difficult to give consideration to robustness and real-time nature.The method comprises: the reference beat signal under the strain-free state of the sensing optical fiber to be measured and the measured beat signal under the strained state are processed respectively, and reference complex Rayleigh scattering signal and measured complex Rayleigh scattering signal containing multiple sensing positions along the optical fiber are obtained;Based on the above two signals, calculate the cross-correlation function;The cross-correlation function corresponding to all sensing positions is stacked along the distance domain, and a two-dimensional cross-correlation thermodynamic map matrix is constructed;The two-dimensional cross-correlation thermodynamic map matrix is filtered and weighted, and the optimal frequency drift is obtained;Based on the optimal frequency drift, the strain distribution along the sensing optical fiber to be measured is obtained by demodulation.The application can realize high robustness, fast demodulation in the application scene with extremely high real-time requirement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical fiber sensing, in particular to a distributed OFDR sensing signal demodulation method and system based on local ridge line tracking. BACKGROUND

[0002] Optical frequency domain reflectometry (OFDR) is a high-precision fiber distributed sensing technology based on tunable laser scanning and optical interference principle, and its core mechanism is similar to that of continuous frequency modulation wave radar (FMCW). The technology realizes the accurate demodulation of the position and amplitude information of the scattering points along the optical fiber by detecting the new frequency signal generated by the coherent beat frequency of the reference light and the Rayleigh backscattering light. Compared with the traditional optical time domain reflectometry (OTDR), OFDR has millimeter-level ultra-high spatial resolution, high sensitivity and high signal-to-noise ratio; compared with the traditional point sensor based on electrical signal, OFDR uses optical fiber as the sensing medium, and naturally has the advantages of anti-electromagnetic interference, corrosion resistance, long transmission distance, etc., and can realize continuous distributed measurement along the whole optical fiber, and has wide application prospects in the fields of aerospace structure monitoring, large civil engineering (bridge, dam), submarine oil pipeline and composite material strain analysis.

[0003] The high spatial resolution of the OFDR system depends on the linearly swept light source provided by the tunable laser. In the ideal state, the laser frequency changes strictly linearly with time, and the reflection signal of a specific position on the measured optical fiber shows a single frequency component in the frequency domain. However, due to the limitations of existing laser manufacturing processes, the actual output of the swept light often has frequency nonlinear effects, which causes the frequency spectrum of the beat signal to spread in the frequency domain, seriously deteriorating the spatial resolution of the system. Therefore, the existing OFDR system usually introduces an auxiliary interferometer (such as a Michelson interferometer) to extract the nonlinear characteristics of the laser sweep, and uses clock resampling technology to compensate for the nonlinearity of the main interferometer signal to ensure accurate mapping of the distance domain information.

[0004] In terms of accurate demodulation of fiber strain or temperature along the line, the current mainstream solution is to use the "sliding window cross-correlation algorithm", which compares the Rayleigh scattering spectrum of the reference signal (zero strain state) and the measurement signal (strain state) in the local window, calculates the wavelength shift of the spectrum, and uses the linear relationship between the wavelength shift and the physical quantity to obtain the strain or temperature information distributed along the optical fiber. Although this method based on cross-correlation is mature in theory and has high precision, it still has significant technical limitations when faced with complex actual engineering environments. SUMMARY

[0005] In view of the above problems, the present application provides a distributed OFDR sensing signal demodulation method and system based on local ridge line tracking.

[0006] According to an aspect of the present application, a distributed OFDR sensing signal demodulation method based on local ridge line tracking is provided, which comprises:

[0007] The reference beat frequency signals obtained by processing the sensing optical fiber in the strain-free state and the measurement beat frequency signals obtained by processing the sensing optical fiber in the strained state are subjected to fast Fourier transform, and the signals are mapped from the time domain to the distance domain to obtain corresponding two distance domain spectra; the two distance domain spectra are traversed and intercepted by using a sliding window, and the distance domain spectrum of each window is subjected to inverse Fourier transform to obtain reference complex Rayleigh scattering signals and measurement complex Rayleigh scattering signals containing multiple sensing positions along the optical fiber;

[0008] The cross-correlation function corresponding to each sensing position on the sensing optical fiber is calculated based on the reference complex Rayleigh scattering signals and the measurement complex Rayleigh scattering signals;

[0009] The cross-correlation functions corresponding to all sensing positions are stacked along the distance domain to construct a two-dimensional cross-correlation thermodynamic map matrix;

[0010] The two-dimensional cross-correlation thermodynamic map matrix is subjected to filtering and weighting processing to obtain the optimal frequency drift of each sensing position on the sensing optical fiber;

[0011] The strain distributed along the sensing optical fiber is demodulated based on the optimal frequency drift.

[0012] Further, the acquisition and processing process of the reference beat frequency signals and the measurement beat frequency signals comprises:

[0013] The main interferometer optical signals and the auxiliary interferometer optical signals of the sensing optical fiber in the strain-free state and the strained state are respectively acquired by using a distributed OFDR sensing device; wherein the strain-free state corresponds to a group of reference optical signals, and the strained state corresponds to a group of measurement optical signals; the main interferometer optical signals are subjected to nonlinear compensation by using the auxiliary interferometer optical signals in the reference optical signals and the measurement optical signals respectively, so as to obtain the reference beat frequency signals and the measurement beat frequency signals after nonlinear compensation.

[0014] Further, the calculation formula of the cross-correlation function corresponding to each sensing position on the sensing optical fiber is:

[0015] ;

[0016] In the formula, represents the cross-correlation function value corresponding to the i th sensing position ; represents the frequency drift of the i th sensing position ; represents the reference complex Rayleigh scattering signal corresponding to the i th sensing position. represents the conjugate transpose of the measured complex Rayleigh scattering signal corresponding to the i-th sensing position; is an inverse Fourier transform operator; represents the modulo operation.

[0017] Further, the filtering and weighting of the two-dimensional cross-correlation heat map matrix comprises: performing spatial convolution on the two-dimensional cross-correlation heat map matrix using a two-dimensional Gaussian filter to obtain a filtered heat map matrix; and weighting the filtered heat map matrix using a Gaussian inertia weight function, and determining the optimal frequency drift of the current sensing position by finding the maximum value after weighting.

[0018] Further, the formula for weighting the filtered heat map matrix using the Gaussian inertia weight function and determining the optimal frequency drift of the current sensing position by finding the maximum value after weighting is:

[0019] ;

[0020] wherein, represents the optimal frequency drift of the i-th sensing position; represents the value of the independent variable when the function value is maximum; represents the filtered heat map matrix corresponding to the i-th sensing position; represents the Gaussian inertia weight function corresponding to the i-th sensing position, , represents the inertia penalty factor, represents the optimal frequency drift of the i-1-th sensing position.

[0021] Further, after obtaining the optimal frequency drift, the frequency drift is taken as the independent variable, and the value obtained by taking the natural logarithm of the local cross-correlation function value based on the optimal frequency drift and the left and right adjacent points is taken as the dependent variable, and a quadratic curve model is fitted using the least square method to obtain the model fitting coefficient , then the final optimal frequency drift is:

[0022] .

[0023] ​Furthermore, the distributed OFDR sensing device includes: a narrow-linewidth tunable laser source, a first fiber coupler, a main interferometer, an auxiliary interferometer, and a data acquisition and signal processing module; the laser emitted by the narrow-linewidth tunable laser source is split into two paths by the first fiber coupler, one path of which enters the main interferometer, which includes a reference arm and a sensing arm; the other path of which enters the auxiliary interferometer; the main interferometer and the auxiliary interferometer respectively output the main interferometer optical signal and the auxiliary interferometer optical signal to the data acquisition and signal processing module;

[0024] The main interferometer includes a second fiber coupler, a fiber circulator, a polarization controller, a third fiber coupler, and a sensing fiber. Light entering the main interferometer is split into two paths by the second fiber coupler: one path enters the reference arm containing the polarization controller, and the other path enters the sensing arm containing the fiber circulator and the sensing fiber. The reference light output from the reference arm and the interference light output from the sensing arm are coupled by the third fiber coupler to output the main interferometer optical signal to the data acquisition and signal processing module. The auxiliary interferometer includes a fourth fiber coupler, a delay fiber, a first Faraday mirror, and a second Faraday mirror. Light entering the auxiliary interferometer is split into two paths by the fourth fiber coupler. Two beams of light enter the delay fiber, then enter the first Faraday mirror and are reflected; the other beam enters the second Faraday mirror and is reflected. The two reflected beams are then coupled by the fourth fiber coupler and output as auxiliary interferometer optical signals to the data acquisition and signal processing module. The data acquisition and signal processing module includes a balanced photodetector, a single-channel photodetector, and a data acquisition card. The main interferometer optical signal enters the balanced photodetector for photoelectric conversion, and the auxiliary interferometer optical signal enters the single-channel photodetector for photoelectric conversion. They are then acquired by the data acquisition card, which outputs the processed main interferometer optical signal and auxiliary interferometer optical signal.

[0025] Furthermore, the calculation formula for obtaining the strain distribution along the sensing fiber under test based on the optimal frequency drift demodulation is as follows:

[0026] ;

[0027] In the formula, This represents the strain at the i-th sensing position; Indicates frequency resolution; This represents the center frequency of a narrow-linewidth tunable laser source in a distributed OFDR sensing device. This represents the strain sensitivity coefficient of the optical fiber under test.

[0028] According to another aspect of the present invention, a distributed OFDR sensor signal demodulation system based on local ridge tracking is proposed, the system being implemented based on the aforementioned distributed OFDR sensor signal demodulation method based on local ridge tracking; the system includes:

[0029] a Rayleigh scattering signal acquisition module configured to perform fast Fourier transform on a reference beat frequency signal obtained by collecting and processing the sensing optical fiber in a strain-free state and a measurement beat frequency signal obtained by collecting and processing the sensing optical fiber in a strained state, to map the signals from a time domain to a distance domain, and to obtain corresponding two distance domain spectra; to traverse and intercept the two distance domain spectra by using a sliding window, to perform inverse Fourier transform on the distance domain spectrum of each window, and to obtain reference complex Rayleigh scattering signals and measurement complex Rayleigh scattering signals containing multiple sensing positions of the optical fiber along a line;

[0030] a cross-correlation calculation module configured to calculate a cross-correlation function corresponding to each sensing position on the sensing optical fiber based on the reference complex Rayleigh scattering signals and the measurement complex Rayleigh scattering signals;

[0031] a heat map construction module configured to stack the cross-correlation functions corresponding to all sensing positions along the distance domain, and to construct a two-dimensional cross-correlation heat map matrix;

[0032] an optimal frequency shift amount calculation module configured to perform filtering processing and weighting processing on the two-dimensional cross-correlation heat map matrix, and to obtain an optimal frequency drift amount of each sensing position on the sensing optical fiber;

[0033] a strain demodulation module configured to demodulate and obtain a strain distribution along the sensing optical fiber based on the optimal frequency drift amount.

[0034] The present application has the following beneficial technical effects:

[0035] The present application provides a distributed OFDR sensing signal demodulation method and system based on local ridge line tracking. First, the clock signal extracted by the auxiliary interferometer is used to perform nonlinear compensation on the main interferometer data to correct the nonlinear sweep error. Then, the traditional one-dimensional peak searching is broken, and the demodulation process is reconstructed as a global energy ridge line tracking problem in a two-dimensional distance-lag heat map. By introducing a physical continuity constraint, the algorithm can automatically avoid noise interference and maintain continuous measurement in deep fading areas. At the same time, a dynamic local search window is designed, and the cross-correlation search space is compressed by more than 99% according to the path prediction, thereby greatly reducing the cross-correlation calculation amount. The present application has simple and efficient logic, and can perfectly adapt to embedded chips and Internet of Things edge computing nodes with extremely low computing power requirement, thereby significantly reducing the deployment cost and energy consumption of the distributed OFDR sensing system. BRIEF DESCRIPTION OF DRAWINGS

[0036] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which a number of embodiments of the application are illustrated by way of example, and in which:

[0037] Figure 1 is a flow chart of a distributed OFDR sensing signal demodulation method based on local ridge line tracking according to an embodiment of the present application;

[0038] Figure 2 is a structural schematic diagram of a distributed OFDR sensing device according to an embodiment of the present application;

[0039] Figure 3 is a schematic diagram of a main interferometer beat signal after non-linear compensation (correction) according to an embodiment of the present application;

[0040] Figure 4 is a schematic diagram of a distance domain spectrum according to an embodiment of the present application;

[0041] Figure 5 is a schematic diagram of a two-dimensional cross-correlation heat map matrix according to an embodiment of the present application;

[0042] Figure 6 is a schematic diagram of an enhanced heat map after two-dimensional Gaussian convolution filtering according to an embodiment of the present application;

[0043] Figure 7 is a schematic diagram of a ridge line tracking path with introduction of physical inertia weight constraint according to an embodiment of the present application;

[0044] Figure 8 is a schematic diagram of sub-pixel positioning based on logarithmic Gaussian fitting according to an embodiment of the present application;

[0045] Figure 9 is a schematic diagram of fiber strain distribution along a line according to an embodiment of the present application;

[0046] Figure 10 is a comparison schematic diagram between a traditional cross-correlation algorithm and the present application according to an embodiment of the present application;

[0047] Figure 11 is a comparison schematic diagram between a traditional cross-correlation algorithm and the present application in search space according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present application, and do not limit the scope of the present application in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0049] The demodulation method of the signal collected by the traditional OFDR sensing system usually adopts a sliding window full spectrum cross-correlation algorithm. The method needs to perform point-by-point full range search on the entire distance domain. The essence is a "greedy search" strategy based on local information, which lacks consideration of the continuity of the overall strain distribution of the optical fiber. In long-distance sensing, due to the birefringence effect of the single-mode optical fiber, the Rayleigh scattering signal is prone to polarization fading phenomenon, which causes the local signal-to-noise ratio to fluctuate sharply or even drop to the noise floor level. At this time, the traditional cross-correlation algorithm is prone to incorrectly locking to the random noise peak with higher amplitude, resulting in non-physical random jumps in the demodulation result, which seriously damages the reliability of the measurement. At the same time, in order to ensure the measurement accuracy, the traditional cross-correlation algorithm needs to perform cross-correlation operation on the full frequency range at thousands of sensing points along the optical fiber. As the spatial resolution requirement increases (i.e. the sliding window step decreases), the number of windows required increases sharply, resulting in massive cross-correlation operation, huge calculation redundancy, long data processing time, and serious restriction on the real-time processing efficiency of the OFDR sensing system, which is difficult to meet the application scenarios with high real-time requirements such as earthquake warning and structure vibration monitoring.

[0050] In order to solve the problem that the traditional cross-correlation algorithm cannot balance between robustness and real-time performance, and in order to realize high-robustness demodulation and fast calculation in a low signal-to-noise ratio and deep fading environment, the application provides a distributed OFDR sensing signal demodulation method and system based on local ridge line tracking. Unlike the traditional one-dimensional peak searching logic, the application constructs a two-dimensional distance-lag cross-correlation thermogram of the reference signal and the measurement signal, converts the distributed sensing demodulation problem into a global optimal path planning problem in image processing, and through the introduction of physical continuity constraint, the algorithm can still keep stable locking to the real signal in deep fading and strong noise interference. At the same time, the dynamic local window mechanism is used to greatly compress the cross-correlation search space. The application significantly reduces the calculation amount and shortens the demodulation time, effectively solves the demodulation jump problem in a low signal-to-noise ratio environment, and provides a new technical path for realizing high-precision, high-robustness and real-time distributed sensing.

[0051] The application embodiment provides a distributed OFDR sensing signal demodulation method based on local ridge line tracking, as shown in the method. Figure 1 The method comprises the following steps.

[0052] S1. The reference beat frequency signal acquired and processed under unstrained state and the measured beat frequency signal acquired and processed under strained state are respectively subjected to Fast Fourier Transform (FFT) to map the signals from the time domain to the range domain, obtaining two corresponding range domain spectra; the two range domain spectra are traversed and truncated using a sliding window, and the range domain spectrum of each window is subjected to Inverse Fourier Transform (IFT) to obtain the reference complex Rayleigh scattering signal and the measured complex Rayleigh scattering signal containing multiple sensing positions along the fiber; the acquisition and processing process of the reference beat frequency signal and the measured beat frequency signal includes:

[0053] Distributed OFDR sensing devices are used to collect the main interferometer optical signals and auxiliary interferometer optical signals of the optical fiber under test in two states: no strain and strain. The no strain state corresponds to a set of reference optical signals, and the strain state corresponds to a set of measurement optical signals. The main interferometer optical signals of the reference optical signals and measurement optical signals are nonlinearly compensated by the auxiliary interferometer optical signals, so as to obtain the nonlinearly compensated reference beat frequency signals and measurement beat frequency signals.

[0054] S2. Calculate the cross-correlation function corresponding to each sensing position on the sensing fiber under test based on the reference complex Rayleigh scattering signal and the measured complex Rayleigh scattering signal;

[0055] S3. Stack the cross-correlation functions corresponding to all sensing positions along the distance domain to construct a two-dimensional cross-correlation heat map matrix;

[0056] S4. Filter and weight the two-dimensional cross-correlation heat map matrix to obtain the optimal frequency drift at each sensing position on the sensing fiber under test.

[0057] S5. Demodulate based on the optimal frequency drift to obtain the strain distributed along the sensing fiber under test.

[0058] The method begins with S1. In S1, the reference beat frequency signal acquired and processed under unstrained conditions and the measured beat frequency signal acquired and processed under strained conditions are respectively subjected to Fast Fourier Transform (FFT) to map the signals from the time domain to the range domain, obtaining two corresponding range domain spectra. The two range domain spectra are then traversed and truncated using a sliding window, and the range domain spectrum of each window is subjected to Inverse Fourier Transform (IFT) to obtain the reference complex Rayleigh scattering signal and the measured complex Rayleigh scattering signal containing multiple sensing positions along the fiber.

[0059] According to an embodiment of the present invention, firstly, a distributed OFDR sensing device is used to collect the main interferometer optical signals and auxiliary interferometer optical signals of the optical fiber under test in both unstrained and strained states; wherein, the unstrained state corresponds to a set of reference optical signals, and the strained state corresponds to a set of measurement optical signals. For example... Figure 2As shown, the distributed OFDR sensing device comprises a narrow linewidth tunable laser source (TLS) 1, a first fiber coupler 2, a main interferometer, an auxiliary interferometer, a data acquisition and signal processing module; the laser emitted by the narrow linewidth tunable laser source 1 is divided into two paths by the first fiber coupler 2, one of which enters the main interferometer, and the main interferometer comprises a reference arm and a sensing arm; the other path of light enters the auxiliary interferometer; the main interferometer and the auxiliary interferometer output main interferometer optical signals and auxiliary interferometer optical signals to the data acquisition and signal processing module respectively; wherein the main interferometer comprises a second fiber coupler 4, a fiber circulator (CIR) 5, a polarization controller (PC) 6, a third fiber coupler 7, a sensing fiber 11; the light entering the main interferometer is divided into two paths by the second fiber coupler 4, one of which enters the reference arm comprising the polarization controller 6, and the other enters the sensing arm comprising the fiber circulator 5 and the sensing fiber 11; the reference light output by the reference arm and the interference light output by the sensing arm are coupled by the third fiber coupler 7 after the reference light and the interference light are coupled, and then output the main interferometer optical signals to the data acquisition and signal processing module; the auxiliary interferometer comprises a fourth fiber coupler 3, a delay fiber 8, a first Faraday reflector (FRM1) 9, a second Faraday reflector (FRM2) 10; the light entering the auxiliary interferometer is divided into two paths by the fourth fiber coupler 3, one of which enters the delay fiber 8, and then enters the first Faraday reflector 9 and is reflected, and the other enters the second Faraday reflector 10 and is reflected; the two paths of reflected light are coupled again by the fourth fiber coupler 3 after being coupled, and then output the auxiliary interferometer optical signals to the data acquisition and signal processing module; the data acquisition and signal processing module comprises a balanced photodetector (BPD) 12, a single-channel photodetector (PD) 13, a data acquisition card (DAQ) 14; the main interferometer optical signals enter the balanced photodetector 12 for photoelectric conversion, and the auxiliary interferometer optical signals enter the single-channel photodetector 13 for photoelectric conversion, and then are collected by the data acquisition card 14, and the data acquisition card 14 outputs the processed main interferometer optical signals and auxiliary interferometer optical signals.

[0060] In this embodiment, the main interferometer is composed of a Mach-Zehnder interferometer (MZI), the upper arm is a reference arm, a polarization controller 6 is placed on the arm to reduce the polarization fading effect, and the lower arm is a measurement arm connected to the sensing fiber 11 through the fiber loop 5. The sensing fiber is a whole single-mode fiber, and the sensing fiber can realize sensing along the line part; the auxiliary interferometer is composed of a Michelson interferometer (MI), which is divided into two paths through the fourth fiber coupler 3. One path is directly connected to the second Faraday mirror 10 to generate reflected light, and the other path passes through a delay fiber 8 with a length of 13 m and is connected to the first Faraday mirror 9. The Michelson interferometer formed by the Faraday rotating mirror structure can greatly reduce the polarization fading effect and enhance the signal quality of the auxiliary interferometer; the data acquisition and signal processing part, the main interferometer optical signal is converted into an electrical signal by the balanced photoelectric detector 12, collected, processed and output by the data acquisition card 14, and the auxiliary interferometer data is converted into an electrical signal by the single-channel photoelectric detector 13, collected, processed and output by the data acquisition card 14, and further signal processing and analysis are performed in the host computer.

[0061] Since the beat frequency signal is superimposed by the sub-beat frequency signals at each position in the time domain, and the center frequency of the sub-beat frequency signal has a linear relationship with the position of the sensing fiber to be measured, the Rayleigh backscattering signal at different positions of the sensing fiber to be measured can be extracted by spectrum analysis, and the strain information of each position of the sensing fiber to be measured can be extracted.

[0062] In this embodiment, the above device is used to collect the time domain beat frequency signal of the main interferometer in the strain-free state as the reference signal , and the time domain beat frequency signal of the auxiliary interferometer as the nonlinear compensation signal of the reference signal; the time domain beat frequency signal of the main interferometer in the strain state is collected as the measurement signal , and the time domain beat frequency signal of the auxiliary interferometer as the nonlinear compensation signal of the measurement signal.

[0063] Then, the reference signal and the measurement signal are used to compensate the nonlinearities of the main interferometer optical signal and the auxiliary interferometer optical signal, respectively, to obtain the nonlinearly compensated reference beat frequency signal and the measurement beat frequency signal.

[0064] In this embodiment, since the optical signal collected by the data acquisition card 14 is Rayleigh scattering spectrum without nonlinear compensation, in order to obtain the compensated Rayleigh scattering spectrum, the original Rayleigh scattering spectrum is compensated by using the existing nonlinear compensation algorithm. Since the auxiliary interferometer does not have a sensing optical fiber, the Rayleigh scattering spectrum obtained by the auxiliary interferometer only contains the nonlinear information of the light source, so the nonlinear information of the swept light source is obtained by using the beat signal of the auxiliary interferometer, and then the nonlinear compensation algorithm is used to compensate the main interferometer to obtain the linear Rayleigh scattering spectrum after compensation. That is, the time domain beat signal of the auxiliary interferometer in the unstrained state is used to compensate the nonlinear information of the main interferometer. The reference signal is nonlinearly compensated , and the time domain beat signal of the auxiliary interferometer in the strained state is used to compensate the nonlinear information of the main interferometer. The measurement signal is nonlinearly compensated , and the nonlinearly compensated beat signals are respectively a reference beat signal and a measurement beat signal .

[0065] Figure 3 The beat signal spectrum obtained after the nonlinear compensation of the main interferometer signal is shown. The original beat signal is formed by superimposing the Rayleigh scattering signals in the sensing optical fiber, and the nonlinear compensation of the scanning is realized by resampling the main interferometer signal by using the beat signal generated by the auxiliary interferometer, so as to obtain the beat signal after nonlinear compensation.

[0066] Then, the reference beat signal and the measurement beat signal are respectively subjected to fast Fourier transform, the signals are mapped from the time domain to the distance domain, and the corresponding two distance domain spectra are obtained; the two distance domain spectra are traversed and intercepted by using a sliding window, the distance domain spectrum of each window is subjected to inverse Fourier transform, so as to obtain the reference complex Rayleigh scattering signal and the measurement complex Rayleigh scattering signal containing multiple sensing positions of the optical fiber along the line.

[0067] The distance domain spectra corresponding to the reference beat signal and the measurement beat signal are shown in Figure 4 , and the distance domain spectrum reflects the position information distribution along the sensing optical fiber.

[0068] Then, S2 is performed, in which the cross-correlation function corresponding to each sensing position on the to-be-measured sensing optical fiber is calculated based on the reference complex Rayleigh scattering signal and the measurement complex Rayleigh scattering signal.

[0069] According to the embodiment of the present application, next, the reference complex Rayleigh scattering signal and the measurement complex Rayleigh scattering signal are used to construct a two-dimensional cross-correlation heat map. The conventional method usually only retains the maximum value after the cross-correlation operation, and discards the remaining spectrum information; the present application retains the full-dimensional cross-correlation information, and completely retains the potential signal characteristics and noise distribution information.

[0070] First, calculate the cross-correlation function corresponding to each sensing location on the sensing fiber under test:

[0071] (1)

[0072] in, Indicates the i-th sensing position on the optical fiber (That is, the cross-correlation function value corresponding to the i-th sliding window); Indicates the i-th sensor position The frequency drift; This is the inverse Fourier transform operator; This represents the reference complex Rayleigh scattering signal at the i-th sensing position; This represents the conjugate transpose of the complex Rayleigh scattering signal measured at the i-th sensing position; This indicates taking the modulus.

[0073] The cross-correlation function values ​​for all sensing locations can be obtained using the formula (1) above.

[0074] Then execute S3, in which the cross-correlation functions corresponding to all sensing positions are stacked along the distance domain to construct a two-dimensional cross-correlation heatmap matrix.

[0075] According to an embodiment of the present invention, the cross-correlation functions of all windows are stacked along the distance domain to construct a two-dimensional cross-correlation heatmap matrix M as follows:

[0076] (2)

[0077] Figure 5 The constructed two-dimensional cross-correlation heatmap is shown. Unlike traditional methods that only calculate the difference spectrum or retain a single maximum value, this invention retains the full-dimensional cross-correlation spectral information of the reference signal and the measured signal within a sliding window. By stacking the cross-correlation spectra of all sensing locations along the distance axis, a grayscale image containing three-dimensional information of "distance-frequency shift-correlation" is constructed. Figure 5 The bright, continuous stripes (ridges) in the middle represent the true strain trajectory distributed along the optical fiber.

[0078] Then, S4 is executed, in which the two-dimensional cross-correlation heat map matrix is ​​filtered and weighted to obtain the optimal frequency drift at each sensing position on the sensing fiber under test.

[0079] According to an embodiment of the present invention, in order to eliminate random speckle noise in the heatmap, the two-dimensional cross-correlation heatmap matrix M is regarded as a grayscale image, and a two-dimensional Gaussian filter is used. Spatial convolution is performed on the two-dimensional cross-correlation heatmap matrix M to obtain the filtered heatmap matrix. :

[0080] (3)

[0081] wherein, is a local spatial coordinate variable inside the Gaussian kernel.

[0082] Further, in order to extract continuous distributed strain under noise background, the present application introduces a Gaussian inertia weight function. Assuming that the optimal frequency drift amount determined by the i-1th window (i.e. the i-1th sensing position) is , then for the i th window, a Gaussian inertia weight function centered at is defined as:

[0083] (4)

[0084] wherein, is an inertia penalty factor, the smaller, the stronger the inhibitory effect on the jump; the larger, the greater the change rate of strain allowed.

[0085] The cross-correlation function of the current position is weighted by using the Gaussian inertia weight function corresponding to the i th sensing position , and the optimal frequency drift amount of the current i th sensing position is determined by finding the maximum value of the weighted function :

[0086] (5)

[0087] wherein, represents the filtered heat map matrix corresponding to the i th sensing position; represents the value of the independent variable when the function value is maximum.

[0088] In order to further improve the measurement accuracy and break through the limitation of discrete pixels, the embodiment of the present application further adopts Gaussian curve fitting to extract sub-pixel frequency shift. After obtaining the optimal frequency drift amount , the frequency drift amount is taken as the independent variable, and the value obtained by taking the natural logarithm of the local cross-correlation function value based on the optimal frequency drift amount and its left and right adjacent points is taken as the dependent variable (ln ), and a quadratic curve model is fitted by using the least square method to obtain the model fitting coefficient ; wherein the quadratic curve model is represented as:

[0089] (6)

[0090] ​Utilizing the fitting coefficients The symmetry axis of the quadratic curve is calculated to obtain the final sub-pixel accurate optimal frequency drift amount For:

[0091] (7)

[0092] Figure 6 , Figure 7 and Figure 8 The effect of the above processing is shown. Figure 6 The enhanced heat map after two-dimensional Gaussian convolution filtering is shown; compared with the original map, the filtering step effectively suppresses the random speckle noise in the background and improves the contrast of the ridge line. Figure 7 The search path after introducing the physical inertia weight (i.e., Gaussian inertia weight function) constraint is shown; by utilizing the spatial continuity of the fiber strain, the off-axis noise interference far from the main ridge line is automatically shielded, and stable tracking of the signal can still be maintained in the area with extremely low signal-to-noise ratio. Figure 8 The sub-pixel positioning result based on the logarithmic Gaussian fitting (i.e., quadratic curve model) is shown; by performing quadratic curve fitting on the energy distribution near the ridge line peak, the limitation of the FFT frequency resolution is broken through, and accurate positioning at the micro-strain level is realized.

[0093] Then S5 is performed, in which the strain along the distribution of the to-be-measured sensing fiber is obtained based on the optimal frequency drift amount demodulation.

[0094] According to the embodiment of the present application, the strain of the i-th sensing position is calculated according to the optimal frequency drift amount , in combination with the center frequency , the frequency resolution of the narrow-linewidth tunable laser light source 1 in the distributed OFDR sensing device, and the strain sensitivity coefficient of the to-be-measured sensing fiber as follows, and the strain along the distribution of the to-be-measured sensing fiber is further obtained:

[0095] (8)

[0096] Figure 9 The final demodulated distributed strain curve is shown, which is the micro-strain distribution obtained by inversion according to the fiber sensitivity coefficient. The results show that the present application can accurately restore the strain state of the fiber along the line, and the curve is smooth and continuous without the common "jumping" error of traditional algorithms.

[0097] Figure 10 The comparison of the stability and noise resistance of the traditional cross-correlation algorithm and the method of the present application is shown.

[0098] The effect of the traditional cross-correlation algorithm is shown in Figure 10 ​As shown in (a), due to the random fading characteristics of Rayleigh scattering signals, the signal-to-noise ratio (SNR) of the traditional cross-correlation algorithm decreases sharply when pursuing high spatial resolution (i.e., reducing the sliding window). At this time, the noise peak in the cross-correlation spectrum is extremely easy to exceed the signal peak, resulting in a large number of random jumps and high-frequency glitch noise in the demodulation result, which cannot accurately reflect the true strain, and the measurement consistency under different window sizes is poor.

[0099] The spine tracking effect of the application is as follows Figure 10 As shown in (b), thanks to the introduction of two-dimensional heat maps and physical inertia weights, the method of the application can effectively utilize the continuity characteristics of strain in space, automatically filter off-axis noise and false peaks. Even under the condition of a very small window (high spatial resolution), the strain curve demodulated by the application is still smooth, continuous and free from bottom noise interference.

[0100] The comparison results fully prove that the method of the application has excellent robustness in a low signal-to-noise ratio environment, and solves the problem that high resolution and high stability cannot be achieved in traditional OFDR demodulation.

[0101] Figure 11 The search space comparison diagram of the traditional cross-correlation algorithm and the method of the application is shown.

[0102] Figure 11 The dark gray background area in (a) represents the global search space required by the traditional cross-correlation algorithm, which needs to calculate all pixel points one by one, and there is a huge calculation redundancy. In contrast, the semi-transparent strip in (b) shows the dynamic local search window proposed in the method of the application, which automatically predicts and locks the signal spine (white curve) by using the physical continuity of the fiber strain and the inertia weight constraint. Figure 11 The results show that the application compresses the effective cross-correlation search space to less than 1% of the full spectrum (calculation compression rate > 99%), and realizes the order of magnitude improvement of system real-time performance on the premise of ensuring the demodulation accuracy.

[0103] In summary, the application proposes a new demodulation method based on computer vision assisted local ridge tracking. Firstly, two signals need to be collected, one is the reference signal, which represents the Rayleigh scattering spectrum when the external physical quantity has not changed, and through which the sensing fiber in the unchanged state is calibrated; one is the measurement signal, which applies a known strain to a fixed point on the sensing fiber, and then processes to obtain the Rayleigh scattering spectrum when the external physical quantity changes; the two groups of signals are subjected to nonlinear compensation and windowed Fourier transform, and are mapped to the distance domain to obtain the Rayleigh scattering complex spectrum corresponding to the real physical position. Unlike the traditional method of directly performing one-dimensional peak searching, the application first constructs a two-dimensional distance-lag cross-correlation heat map of the reference signal and the measurement signal on the full fiber length, so as to retain all potential signal feature and noise distribution information; then, the distributed strain demodulation problem is reconstructed as a global optimal path planning problem in the heat map, an energy functional including a cross-correlation energy term and a physical continuity constraint term is constructed, and the dynamic programming idea is used to track the ridge path with the maximum energy along the distance axis; in the tracking process, a dynamic local window mechanism is introduced, the search range of the current position is predicted according to the demodulation result of the last position, and the cross-correlation is calculated only in the local window near the predicted path, so as to avoid noise interference in the non-interest area and greatly compress the calculation amount; finally, the locked ridge path is output as the strain size distributed along the fiber. Compared with the traditional full-spectrum cross-correlation method, the application completely eliminates the random jump error under low signal-to-noise ratio by using the physical continuity constraint, and at the same time, the calculation space is compressed by more than 99% through the local window mechanism, which significantly improves the robustness and real-time response ability of the system.

[0104] The application not only significantly improves the real-time response ability and measurement accuracy of the OFDR system, but also overcomes the demodulation jump error of the traditional OFDR system in a low signal-to-noise ratio environment. Secondly, it realizes the true "software-defined high-performance sensing" without relying on polarization diversity reception, and only by means of signal processing algorithm can continuous and reliable measurement data be obtained in deep fading and environmental noise working conditions, which significantly reduces the integration cost and deployment threshold of the system; realizes high-precision and blind-area-free continuous measurement along the whole fiber, and provides a new distributed monitoring method with high reliability and high cost performance for bridge, dam, oil pipeline, earthquake monitoring and other fields, which has very high engineering popularization value.

[0105] The embodiment of the application also proposes a distributed OFDR sensing signal demodulation system based on local ridge tracking, which is realized based on the distributed OFDR sensing signal demodulation method based on local ridge tracking described in the above embodiment; the system comprises:

[0106] a Rayleigh scattering signal acquisition module configured to perform fast Fourier transform on a reference beat frequency signal obtained by collecting and processing the sensing optical fiber in a strain-free state and a measurement beat frequency signal obtained by collecting and processing the sensing optical fiber in a strained state, map the signals from a time domain to a distance domain, and obtain corresponding two distance domain spectra; perform traversal interception on the two distance domain spectra using a sliding window, perform inverse Fourier transform on the distance domain spectrum of each window, and thus obtain a reference complex Rayleigh scattering signal and a measurement complex Rayleigh scattering signal containing multiple sensing positions of the optical fiber along a line;

[0107] a cross-correlation calculation module configured to calculate a cross-correlation function corresponding to each sensing position on the sensing optical fiber based on the reference complex Rayleigh scattering signal and the measurement complex Rayleigh scattering signal;

[0108] a heat map construction module configured to stack the cross-correlation functions corresponding to all sensing positions along the distance domain, and construct a two-dimensional cross-correlation heat map matrix;

[0109] an optimal frequency shift amount calculation module configured to perform filtering processing and weighting processing on the two-dimensional cross-correlation heat map matrix, and obtain an optimal frequency drift amount of each sensing position on the sensing optical fiber;

[0110] a strain demodulation module configured to demodulate and obtain a strain distribution along the sensing optical fiber based on the optimal frequency drift amount.

[0111] The function of the distributed OFDR sensing signal demodulation system based on local ridge line tracking described in the embodiments of the present application can be explained by the foregoing distributed OFDR sensing signal demodulation method based on local ridge line tracking, and therefore the unexplained parts of the system embodiments can be referred to the method embodiments described above, and will not be described here again.

[0112] Although the spirit and principles of the present application have been described with reference to several specific embodiments, it should be understood that the present application is not limited to the disclosed specific embodiments, and the division of aspects does not mean that the features in these aspects cannot be combined for the benefit, but only for the convenience of expression. The present application is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the appended claims.

Claims

1. A distributed OFDR sensor signal demodulation method based on local ridgeline tracking, characterized in that, include: The reference beat frequency signal acquired and processed under the strain-free state and the measured beat frequency signal acquired and processed under the strain-treated fiber under test are respectively subjected to fast Fourier transform to map the signal from the time domain to the range domain, and obtain the corresponding two range domain spectra. The acquisition and processing of the reference beat frequency signal and the measured beat frequency signal includes: using a distributed OFDR sensing device to acquire the main interferometer optical signal and the auxiliary interferometer optical signal of the optical fiber under test in two states: no strain and strain; wherein, the no strain state corresponds to a set of reference optical signals and the strain state corresponds to a set of measured optical signals; in both the reference optical signal and the measured optical signal, the auxiliary interferometer optical signal is used to perform nonlinear compensation on the main interferometer optical signal, thereby obtaining the nonlinearly compensated reference beat frequency signal and the measured beat frequency signal; By using a sliding window to traverse and extract the two range domain spectra, and performing an inverse Fourier transform on the range domain spectrum of each window, a reference complex Rayleigh scattering signal and a measured complex Rayleigh scattering signal containing multiple sensing locations along the fiber are obtained. The cross-correlation function corresponding to each sensing position on the sensing fiber under test is calculated based on the reference complex Rayleigh scattering signal and the measured complex Rayleigh scattering signal; the calculation formula for the cross-correlation function corresponding to each sensing position on the sensing fiber under test is as follows: ； In the formula, Indicates the i-th sensor position The corresponding cross-correlation function value; Indicates the i-th sensor position The frequency drift; This represents the reference complex Rayleigh scattering signal corresponding to the i-th sensing position; This represents the conjugate transpose of the complex Rayleigh scattering signal corresponding to the i-th sensing position; This is the inverse Fourier transform operator; Indicates modulo; Stack the cross-correlation functions corresponding to all sensing locations along the distance domain to construct a two-dimensional cross-correlation heatmap matrix; The optimal frequency drift at each sensing position on the optical fiber under test is obtained by filtering and weighting the two-dimensional cross-correlation heatmap matrix. This includes: performing spatial convolution processing on the two-dimensional cross-correlation heatmap matrix using a two-dimensional Gaussian filter to obtain a filtered heatmap matrix; weighting the filtered heatmap matrix using a Gaussian inertial weighting function; and determining the optimal frequency drift at the current sensing position by finding the maximum value after weighting. The strain distributed along the sensing fiber under test is obtained by demodulation based on the optimal frequency drift.

2. The distributed OFDR sensor signal demodulation method based on local ridge tracing according to claim 1, characterized in that, The formula for determining the optimal frequency drift at the current sensing position by weighting the filtered heatmap matrix using a Gaussian inertial weighting function and finding the maximum weighted value is as follows: ； In the formula, This represents the optimal frequency drift at the i-th sensing position; This means finding the independent variable when the function value is maximized. The value; This represents the filtered heatmap matrix corresponding to the i-th sensing position; This represents the Gaussian inertial weight function corresponding to the i-th sensing position. , Indicates the inertia penalty factor. This represents the optimal frequency drift at the (i-1)th sensing position.

3. The distributed OFDR sensor signal demodulation method based on local ridge tracking according to claim 2, characterized in that, After obtaining the optimal frequency drift, the frequency drift is used as the independent variable, and the local cross-correlation function values ​​extracted based on the optimal frequency drift and its left and right adjacent points, with the natural logarithm of the result, are used as the dependent variable. The least squares method is then used to fit a quadratic curve model to obtain the model fitting coefficients. Then the final optimal frequency drift amount for: 。 4. The distributed OFDR sensor signal demodulation method based on local ridge tracing according to claim 1, characterized in that, The distributed OFDR sensing device includes: a narrow-linewidth tunable laser source (1), a first fiber coupler (2), a main interferometer, an auxiliary interferometer, and a data acquisition and signal processing module; the laser emitted by the narrow-linewidth tunable laser source (1) is split into two paths by the first fiber coupler (2), one path of which enters the main interferometer, which includes a reference arm and a sensing arm; the other path of which enters the auxiliary interferometer; the main interferometer and the auxiliary interferometer output the main interferometer optical signal and the auxiliary interferometer optical signal to the data acquisition and signal processing module, respectively; The main interferometer includes a second fiber coupler (4), a fiber circulator (5), a polarization controller (6), a third fiber coupler (7), and a sensing fiber (11). The light entering the main interferometer is split into two paths by the second fiber coupler (4). One path enters the reference arm containing the polarization controller (6), and the other path enters the sensing arm containing the fiber circulator (5) and the sensing fiber (11). The reference light output from the reference arm and the interference light output from the sensing arm are coupled by the third fiber coupler (7) and output as the main interferometer optical signal to the data acquisition and signal processing module. The auxiliary interferometer includes a fourth fiber coupler (3), a delay fiber (8), a first Faraday mirror (9), and a second Faraday mirror (10). The light entering the auxiliary interferometer is split into two paths by the second fiber coupler (4). 3) The light is divided into two paths. One path enters the delay fiber (8), then enters the first Faraday reflector (9) and is reflected. The other path enters the second Faraday reflector (10) and is reflected. The two reflected paths are then coupled by the fourth fiber coupler (3) and output as auxiliary interferometer light signals to the data acquisition and signal processing module. The data acquisition and signal processing module includes a balanced photodetector (12), a single-channel photodetector (13), and a data acquisition card (14). The main interferometer light signal enters the balanced photodetector (12) for photoelectric conversion, and the auxiliary interferometer light signal enters the single-channel photodetector (13) for photoelectric conversion. Then it is acquired by the data acquisition card (14). The data acquisition card (14) outputs the processed main interferometer light signal and auxiliary interferometer light signal.

5. The distributed OFDR sensor signal demodulation method based on local ridge tracking according to claim 3, characterized in that, The formula for calculating the strain distribution along the sensing fiber obtained by demodulation based on the optimal frequency drift is as follows: ； In the formula, This represents the strain at the i-th sensing position; Indicates frequency resolution; The center frequency of the narrow linewidth tunable laser source (1) in the distributed OFDR sensing device is indicated; This represents the strain sensitivity coefficient of the optical fiber under test.

6. A distributed OFDR sensor signal demodulation system based on local ridgeline tracking, characterized in that, The system is implemented based on the distributed OFDR sensing signal demodulation method based on local ridge tracing as described in any one of claims 1-5; the system includes: The Rayleigh scattering signal acquisition module is configured to perform Fast Fourier Transform on the reference beat frequency signal acquired and processed in the unstrained state and the measured beat frequency signal acquired and processed in the strained state of the sensing fiber under test, respectively, to map the signal from the time domain to the range domain and obtain the corresponding two range domain spectra; the two range domain spectra are traversed and truncated using a sliding window, and the range domain spectrum of each window is subjected to inverse Fourier Transform, thereby obtaining the reference complex Rayleigh scattering signal and the measured complex Rayleigh scattering signal containing multiple sensing positions along the fiber; The cross-correlation calculation module is configured to calculate the cross-correlation function corresponding to each sensing position on the sensing fiber under test based on the reference complex Rayleigh scattering signal and the measured complex Rayleigh scattering signal. The heatmap construction module is configured to stack the cross-correlation functions corresponding to all sensing locations along the distance domain to construct a two-dimensional cross-correlation heatmap matrix. The optimal frequency shift calculation module is configured to perform filtering and weighting processing on the two-dimensional cross-correlation heat map matrix to obtain the optimal frequency shift at each sensing position on the sensing fiber under test. The strain demodulation module is configured to demodulate the strain distributed along the sensing fiber under test based on the optimal frequency drift.

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