Signal source two-dimensional trajectory tracking and multi-source signal separation method and system
By constructing the backward Rayleigh scattering space-time three-dimensional model and sliding window indexing technology in optical fiber vibration sensing technology, the problems of mobile signal source positioning and multi-source signal separation in traditional technology are solved, and the accurate tracking of space-time motion trajectory and signal separation are achieved.
Patent Information
- Application Number
- CN202411683848.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-22
- Publication Date
- 2025-03-25
AI Technical Summary
Traditional optical fiber vibration sensing technology is difficult to effectively locate and track the two-dimensional motion trajectory of mobile signal sources, and separate signals from different signal sources in a multi-source signal environment.
By constructing a backward Rayleigh scattering space-time three-dimensional model of the signal to be tested, and using sliding window index technology to perform spatiotemporal dynamic analysis, tracking the spatiotemporal motion trajectory of the mobile signal source. For multi-source signals, components with similar frequencies are separated by decomposition of frequency energy components and short-time Fourier transformation.
It realizes accurate tracking of the space-time two-dimensional motion trajectory of mobile signal sources and effective separation of multi-source signals, improving the accuracy of signal source positioning and the accuracy of signal separation.
Smart Images
Figure CN119691543A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of security detection technology, and in particular to a method and system for two-dimensional trajectory tracking of signal sources and separation of multi-source signals. Background Art
[0002] In recent years, with the rapid development of fiber optic sensing technology, distributed fiber optic vibration sensing detection technology has been widely used by researchers in the security field due to its advantages such as wide detection range, anti-electromagnetic interference, and corrosion and high temperature resistance.
[0003] When there is a signal source in the outside world, the vibration signal generated by the signal source will be transmitted to the optical fiber along the medium, and the backward Rayleigh scattered light signal in the optical fiber will be modulated. By demodulating the backward Rayleigh scattered signal at different positions of the sensing optical fiber, the detection of the external signal source on the entire optical fiber link can be realized. However, traditional demodulation technology methods process one-dimensional backward Rayleigh light scattering signals, which often loses part of the spatiotemporal characteristic information, resulting in low positioning accuracy of the target signal source. At the same time, in actual engineering applications, the position of most signal sources is not fixed. It is still a challenging problem to accurately locate the mobile signal source and track its two-dimensional motion trajectory in time and space.
[0004] On the other hand, most of the current distributed fiber optic vibration sensing monitoring technologies are mainly aimed at detecting and identifying a single signal source. However, due to the complex application environment and diverse event sources, when multiple vibration events occur simultaneously at close range, the vibration ranges of multiple vibration signals overlap with each other, and the vibration signals will be mixed together, making it difficult to identify and separate signals from different signal sources from multiple source signals. Summary of the invention
[0005] The present application provides a method and system for signal source two-dimensional trajectory tracking and multi-source signal separation, which can identify and separate signals from different signal sources from multi-source signals, and track the spatiotemporal two-dimensional motion trajectory of the mobile signal source.
[0006] In a first aspect, an embodiment of the present application provides a method for two-dimensional trajectory tracking of a signal source and separation of multi-source signals, the method comprising:
[0007] Construct a three-dimensional space-time model of the backscattered Rayleigh scattering corresponding to the signal to be tested, and perform space-time dynamic analysis through sliding window indexing technology. If the signal to be tested belongs to a mobile source, track the space-time motion trajectory of each signal source according to the space-time distribution characteristics;
[0008] The frequency energy components of the signal to be tested are decomposed. If the signal to be tested belongs to multiple sources, the parts with similar or overlapping frequency energies in the signal to be tested are analyzed. According to each fixed single frequency energy component, the time domain segment signal set corresponding to the signal to be tested is obtained, and the aliased signals therein are marked to separate the components with similar frequencies in each aliased signal.
[0009] In combination with the first aspect, in one implementation, the step of constructing a three-dimensional space-time model of Rayleigh backscattering corresponding to the signal to be measured includes:
[0010] Obtaining a first measurement duration according to the optical cable detection distance, the number of detection light pulses, and the detection light pulse frequency corresponding to the signal to be measured;
[0011] According to the Rayleigh backscattering curve corresponding to the signal to be measured within the first measurement duration, a three-dimensional space-time Rayleigh backscattering model corresponding to the signal to be measured is constructed in a temporal and spatial order.
[0012] In combination with the first aspect, in one implementation, the performing spatiotemporal dynamic analysis by using a sliding window indexing technique includes:
[0013] Constructing a time domain sliding window and a space domain sliding window respectively along the time axis and the space distance axis of the backward Rayleigh scattering space-time three-dimensional model;
[0014] Slide each spatial domain sliding window along the time axis of the backward Rayleigh scattering space-time three-dimensional model with a preset step length to obtain a plurality of time series segments;
[0015] Perform time domain differential demodulation on each time series segment to determine whether there is a vibration signal at the spatial position corresponding to the time series segment;
[0016] All time series segments with vibration signals are inverted in time and space order to obtain the time and space distribution characteristics of the signal to be measured.
[0017] In combination with the first aspect, in one implementation, tracking the spatiotemporal motion trajectory of each signal source according to the spatiotemporal distribution characteristics includes:
[0018] Invert the spatiotemporal distribution characteristics in time and space order to generate a spatiotemporal distribution diagram corresponding to the signal to be measured;
[0019] In the time-space distribution diagram corresponding to the signal to be measured, the time-space motion trajectory of each signal source is obtained by fitting through the least square fitting method.
[0020] In combination with the first aspect, in one implementation, decomposing the frequency energy components of the signal to be measured includes:
[0021] The frequency energy components of the signal to be measured are decomposed by multi-scale wavelet packet decomposition technology to obtain the energy frequency components contained in the signal to be measured;
[0022] If the signal to be measured contains only a single energy frequency component, the signal to be measured belongs to a single source;
[0023] If the signal to be measured contains multiple energy frequency components, the signal to be measured belongs to multiple sources.
[0024] In combination with the first aspect, in one implementation, analyzing the parts of the signal to be tested with similar or overlapping frequency energies, obtaining a time domain segment signal set corresponding to the signal to be tested according to each fixed single frequency energy component, marking the aliased signals therein, and separating the components with similar frequencies in each aliased signal, includes:
[0025] By using short-time Fourier transform to analyze the parts of the measured signal with similar or overlapping frequency energies, an inverse Fourier transform is performed on each fixed single frequency energy component to obtain a time domain segment signal set corresponding to the measured signal;
[0026] Each time domain segment signal in the time domain segment signal set is sequentially subjected to time domain cross-correlation calculation with the signal to be tested, and the time domain segment signals whose calculation results are lower than the preset cross-correlation coefficient threshold are marked as aliasing signals. The components with similar frequencies in each aliasing signal are separated by spectral subtraction, and the separated aliasing signals are amplified by time domain integration.
[0027] In combination with the first aspect, in one implementation, after the frequency energy components of the signal to be measured are decomposed, the method further includes:
[0028] Performing multi-domain feature analysis on the signal to be tested according to the type of the signal to be tested, obtaining a multi-dimensional feature vector corresponding to the signal to be tested; the type of the signal to be tested includes a single signal fixed source, a single signal mobile source, multiple signal fixed sources and multiple signal mobile sources;
[0029] Pattern recognition is performed on the signal to be tested according to the multidimensional feature vector corresponding to the signal to be tested to obtain the event type corresponding to the signal to be tested.
[0030] In combination with the first aspect, in one implementation, performing multi-domain feature analysis on the signal to be tested according to the type of the signal to be tested includes:
[0031] According to the type of the signal to be measured, feature analysis is performed in the time domain, frequency domain, time-space domain, time-frequency domain and wavelet domain of the signal to be measured.
[0032] In combination with the first aspect, in one implementation manner, after obtaining the multidimensional feature vector corresponding to the signal to be tested, the method further includes:
[0033] The multidimensional feature vector corresponding to the signal to be measured is reduced in dimension by the principal component analysis method to remove the redundancy in the multidimensional feature vector corresponding to the signal to be measured.
[0034] In a second aspect, an embodiment of the present application provides a signal source two-dimensional trajectory tracking and multi-source signal separation system based on any one of the above methods, the system comprising:
[0035] A construction module, used to construct a three-dimensional space-time model of backward Rayleigh scattering corresponding to the signal to be measured;
[0036] The positioning module is used to perform spatiotemporal dynamic analysis on the signal to be measured by using the sliding window indexing technology. If the signal to be measured belongs to a mobile source, the spatiotemporal motion trajectory of each signal source is tracked according to the spatiotemporal distribution characteristics;
[0037] The separation module is used to decompose the frequency energy components of the signal to be tested. If the signal to be tested belongs to multiple sources, the parts with similar or overlapping frequency energies in the signal to be tested are analyzed. According to each fixed single frequency energy component, a time domain segment signal set corresponding to the signal to be tested is obtained, and the aliased signals therein are marked to separate the components with similar frequencies in each aliased signal.
[0038] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:
[0039] This application constructs a three-dimensional spatiotemporal model of backward Rayleigh scattering corresponding to the signal to be tested, and performs spatiotemporal dynamic analysis through sliding window indexing technology. When the signal to be tested belongs to a mobile source, the spatiotemporal motion trajectory of each signal source is tracked according to the spatiotemporal distribution characteristics. When the signal to be tested is a multi-source signal, the parts with similar or overlapping frequency energies in the signal to be tested are analyzed, and the time domain segment signal set corresponding to the signal to be tested is obtained according to each fixed single frequency energy component, and the aliased signals therein are marked, and the components with similar frequencies in each aliased signal are separated, so as to identify and separate signals from different signal sources. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Figure 1 It is a flowchart of a method for two-dimensional trajectory tracking of signal sources and separation of multi-source signals according to an embodiment of the present application;
[0041] Figure 2 This is a three-dimensional spatiotemporal model diagram of backward Rayleigh scattering according to an embodiment of the present application;
[0042] Figure 3 This is a schematic diagram of the structure of a signal source two-dimensional trajectory tracking and multi-source signal separation system according to an embodiment of the present application;
[0043] Figure 4 This is a schematic diagram of the operation flow of the signal source positioning module of the embodiment of the present application;
[0044] Figure 5 This is a schematic diagram of the operation flow of the signal feature extraction and analysis module and the signal source pattern recognition module in the embodiment of the present application. DETAILED DESCRIPTION
[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0046] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.
[0047] First, please refer to Figure 1 , Figure 1 : This is a flow chart of a method for two-dimensional trajectory tracking of signal sources and separation of multi-source signals according to an embodiment of the present application. The method for two-dimensional trajectory tracking of signal sources and separation of multi-source signals provided in this embodiment includes the following steps:
[0048] Step S1: constructing a three-dimensional space-time model of backward Rayleigh scattering corresponding to the signal to be measured.
[0049] Step S2: Perform spatiotemporal dynamic analysis on the backward Rayleigh scattering spatiotemporal three-dimensional model through the sliding window indexing technology, and judge whether the signal to be tested belongs to a mobile source or a fixed source according to the spatiotemporal distribution characteristics of the signal to be tested. If the signal to be tested belongs to a fixed source, proceed to step S3; if the signal to be tested belongs to a mobile source, proceed to step S4.
[0050] Step S3: Locate the position of each signal source according to the temporal and spatial distribution characteristics of the signal to be measured, and then proceed to step S5.
[0051] Step S4: Track the spatiotemporal motion trajectory of each signal source according to the spatiotemporal distribution characteristics of the signal to be measured, and then proceed to step S5.
[0052] Step S5: Decompose the frequency energy components of the signal to be tested to determine whether the signal to be tested belongs to a single source or multiple sources. If the signal to be tested belongs to a single source, end this process; if the signal to be tested belongs to multiple sources, proceed to step S6.
[0053] Step S6: Analyze the parts of the signal to be tested with similar or overlapping frequency energies, obtain a time domain segment signal set corresponding to the signal to be tested according to each fixed single frequency energy component, mark the aliased signals therein, and separate the components with similar frequencies in each aliased signal.
[0054] At present, the separation technology for multi-source mixed signals is mainly divided into two categories. The first category is to perform spectral analysis on the signal to be identified, select multiple groups of filters and set the parameters of each group of filters, and classify multiple signals of different frequencies in the mixed signal through the set multiple groups of filters. However, this technology needs to set the filter parameters of the filter according to the frequency characteristics of the target signal in advance, and has poor ability to distinguish mixed signal sources with similar frequencies. Its stability and practicality need to be improved. The second category is to use the detector to collect different types of single-source vibration signals and then perform linear mixing to construct a mixed data signal set with different numbers of mixed sources. The mixed signals with different numbers of mixed sources are used to train the separation model offline, estimate the number of signal sources in the mixed signal, and select the separation model corresponding to the number of sources to separate the mixed signal. The disadvantage of this algorithm is that the construction cost of the mixed signal data set is high and the complexity of the separation models of different mixed signals is high.
[0055] In order to solve the problems existing in the prior art, the present application constructs a three-dimensional spatiotemporal model of backward Rayleigh scattering corresponding to the signal to be tested, and performs spatiotemporal dynamic analysis through sliding window indexing technology. When the signal to be tested belongs to a mobile source, the spatiotemporal motion trajectory of each signal source is tracked according to the spatiotemporal distribution characteristics. When the signal to be tested is a multi-source signal, the parts with similar or overlapping frequency energies in the signal to be tested are analyzed, and the time domain segment signal set corresponding to the signal to be tested is obtained according to each fixed single frequency energy component, and the aliased signals therein are marked, and the components with similar frequencies in each aliased signal are separated, so as to identify and separate signals from different signal sources.
[0056] In some embodiments, in the above step S1, constructing a three-dimensional space-time model of Rayleigh backscattering corresponding to the signal to be measured includes the following steps:
[0057] S11: Obtain a first measurement duration according to the optical cable detection distance, the number of detection light pulses, and the detection light pulse frequency corresponding to the signal to be measured.
[0058] S12: constructing a three-dimensional space-time model of the Rayleigh backscattering corresponding to the signal to be measured according to the Rayleigh backscattering curve corresponding to the signal to be measured within the first measurement time in a temporal and spatial order.
[0059] In a more specific embodiment, in the above step S11, the formula for calculating the first measurement duration is as follows:
[0060]
[0061] In formula (1), N is the number of detection light pulses, v is the detection light pulse frequency, and T is the first measurement duration.
[0062] In a more specific embodiment, in the above step S12, the backscattering space-time three-dimensional model is shown in Figure 2 , Figure 2 This is a three-dimensional space-time model diagram of backward Rayleigh scattering in an embodiment of the present application.
[0063] exist Figure 2 In, q 1 ~q n is the time domain sliding window, τ is the interval of the time domain sliding window, h is the width of the time domain sliding window, P 1 ~P n is the spatial domain sliding window, l is the interval of the spatial domain sliding window, and d is the width of the spatial domain sliding window.
[0064] In some embodiments, in the above step S2, the backscattering space-time three-dimensional model is subjected to a spatiotemporal dynamic analysis by a sliding window indexing technique, comprising the following steps:
[0065] S21: constructing a time domain sliding window and a space domain sliding window along the time axis and the space distance axis of the above-mentioned backward Rayleigh scattering space-time three-dimensional model respectively.
[0066] S22: Slide each spatial domain sliding window along the time axis of the above-mentioned backward Rayleigh scattering space-time three-dimensional model with a preset step size to obtain a plurality of time series segments.
[0067] S23: Perform time domain differential demodulation on each time sequence segment to determine whether there is a vibration signal at the spatial position corresponding to the time sequence segment.
[0068] S24: Invert all time series segments with vibration signals in time and space order to obtain the time and space distribution characteristics of the signal to be measured.
[0069] In some embodiments, in the above step S21, after constructing the time domain sliding window and the space domain sliding window along the time axis and the space distance axis of the above backward Rayleigh scattering space-time three-dimensional model, respectively, it is also necessary to set the width and interval of the time domain sliding window and the space domain sliding window. The number of the time domain sliding window and the space domain sliding window can be calculated respectively by the following formula:
[0070]
[0071] In formula (2), n is the number of time domain sliding windows, T is the first measurement duration, τ is the interval of the time domain sliding window, and h is the width of the time domain sliding window.
[0072] In formula (3), m is the number of spatial domain sliding windows, L is the optical cable detection distance, l is the interval of the spatial domain sliding windows, and d is the width of the spatial domain sliding windows.
[0073] In a more specific embodiment, in the above step S22, the total number of spatial domain sliding windows is m, and each spatial domain sliding window slides along the time axis, the moving step length of each sliding is τ, and n time series segments are obtained each time sliding.
[0074] In some embodiments, in the above step S3, locating the position of each signal source according to the spatiotemporal distribution characteristics of the signal to be detected includes the following steps:
[0075] The spatial domain sliding window where the temporal and spatial distribution characteristics of the signal to be measured are located is determined, and the spatial position corresponding to the spatial domain sliding window is marked as the position of the signal source.
[0076] In some embodiments, in the above step S4, tracking the spatiotemporal motion trajectory of each signal source according to the spatiotemporal distribution characteristics of the signal to be detected includes the following steps:
[0077] S41: Invert the spatiotemporal distribution characteristics in time and space order to generate a spatiotemporal distribution diagram corresponding to the signal to be tested.
[0078] S42: In the time-space distribution diagram corresponding to the signal to be measured, the time-space motion trajectory of each signal source is obtained by fitting through the least square fitting method.
[0079] In some embodiments, in the above step S41, when generating the spatiotemporal distribution diagram corresponding to the signal to be tested, the following steps are also included:
[0080] The overlapping parts of the spatial domain sliding windows are averaged to improve the accuracy of the spatiotemporal distribution map.
[0081] In some embodiments, in the above step S5, the frequency energy components of the signal to be measured are decomposed to determine whether the signal to be measured belongs to a single source or multiple sources, including the following steps:
[0082] The frequency energy components of the signal to be measured are decomposed by multi-scale wavelet packet decomposition technology to obtain the energy frequency components contained in the signal to be measured.
[0083] If the signal to be measured contains only a single energy frequency component, the signal to be measured belongs to a single source.
[0084] If the signal to be measured contains multiple energy frequency components, the signal to be measured belongs to multiple sources.
[0085] Specifically, the parameters of the multi-scale wavelet packet decomposition technology can be set according to actual application requirements, and the parameters include the number of wavelet packet scales and the wavelet basis function.
[0086] In some embodiments, after the above step S5 is completed, the following steps are further included:
[0087] First, a multi-domain feature analysis is performed on the signal to be tested according to the type of the signal to be tested to obtain a multi-dimensional feature vector corresponding to the signal to be tested. The types of the signal to be tested include single signal fixed source, single signal mobile source, multiple signal fixed source and multiple signal mobile source.
[0088] Afterwards, pattern recognition is performed on the signal to be tested according to the multi-dimensional feature vector corresponding to the signal to be tested, and the event type corresponding to the signal to be tested is obtained.
[0089] In some embodiments, performing multi-domain feature analysis on the signal to be tested according to the type of the signal to be tested includes the following steps:
[0090] According to the type of the signal to be tested, characteristic analysis is performed on the time domain, frequency domain, time-space domain, time-frequency domain and wavelet domain of the signal to be tested.
[0091] In some embodiments, after obtaining the multidimensional feature vector corresponding to the signal to be tested, the following steps are also included:
[0092] The multidimensional feature vector corresponding to the signal to be measured is reduced in dimension by the principal component analysis method to remove the redundancy in the multidimensional feature vector corresponding to the signal to be measured.
[0093] In some embodiments, in the above step S6, analyzing the parts of the signal to be tested with similar or overlapping frequency energies, obtaining a time domain segment signal set corresponding to the signal to be tested according to each fixed single frequency energy component, marking the aliased signals therein, and separating the components with similar frequencies in each aliased signal, comprises the following steps:
[0094] S61: Analyze the parts of the measured signal with similar or overlapping frequency energies by short-time Fourier transform, perform inverse Fourier transform on each fixed single frequency energy component, and obtain a time domain segment signal set corresponding to the measured signal.
[0095] S62: performing time domain cross-correlation calculation on each time domain segment signal in the time domain segment signal set and the signal to be tested in turn, and marking the time domain segment signal whose calculation result is lower than a preset cross-correlation coefficient threshold as an aliasing signal.
[0096] S63: Separate the components with similar frequencies in each aliasing signal by spectral subtraction, and amplify the separated aliasing signals by time domain integration.
[0097] Specifically, in the above step S61, firstly, the time-frequency energy distribution information of the signal to be tested is obtained by short-time Fourier transform, and the time-frequency energy distribution characteristics of the signal are analyzed to find a fixed single frequency energy component within a certain time interval and mark it as G. i (i=1, 2, ....), perform inverse Fourier transform on each fixed single frequency energy component to obtain the time domain segment signal P corresponding to each fixed single frequency energy component i .P i =Fourier(G i ). i and P iThey are respectively the i-th fixed single frequency energy component and the time domain segment signal corresponding to the i-th fixed single frequency energy component.
[0098] Specifically, in the above step S63, the components with similar frequencies of the aliased signal are separated according to the single frequency component information corresponding to the aliased signal in combination with spectral subtraction. The separated aliased signal is amplified by time domain integration in order to improve the quality of the signal.
[0099] In a second aspect, the present application provides a signal source two-dimensional trajectory tracking and multi-source signal separation system, the system comprising the following modules:
[0100] The construction module is used to construct a three-dimensional space-time model of backward Rayleigh scattering corresponding to the signal to be measured.
[0101] The positioning module is used to perform spatiotemporal dynamic analysis on the signal to be tested through the sliding window indexing technology, and judge whether the signal to be tested belongs to a mobile source or a fixed source according to the spatiotemporal distribution characteristics of the signal to be tested. If the signal to be tested belongs to a mobile source, the spatiotemporal motion trajectory of each signal source is tracked according to the spatiotemporal distribution characteristics.
[0102] The separation module is used to decompose the frequency energy components of the signal to be tested, and determine whether the signal to be tested belongs to a single source or multiple sources according to the frequency energy components of the signal to be tested. If the signal to be tested belongs to multiple sources, the parts with similar or overlapping frequency energies in the signal to be tested are analyzed, and the time domain segment signal set corresponding to the signal to be tested is obtained according to each fixed single frequency energy component, and the aliased signals therein are marked, and the components with similar frequencies in each aliased signal are separated.
[0103] In some embodiments, the above system further includes the following modules:
[0104] The feature extraction module is used to perform multi-domain feature analysis on the signal to be tested according to the type of the signal to be tested, and obtain a multi-dimensional feature vector corresponding to the signal to be tested. The types of the signal to be tested include single signal fixed source, single signal mobile source, multiple signal fixed source and multiple signal mobile source.
[0105] The pattern recognition module is used to perform pattern recognition on the signal to be tested according to the multi-dimensional feature vector corresponding to the signal to be tested, so as to obtain the event type corresponding to the signal to be tested.
[0106] In a more specific embodiment, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of the signal source two-dimensional trajectory tracking and multi-source signal separation system of the embodiment of the present application. The signal source two-dimensional trajectory tracking and multi-source signal separation system of the embodiment includes the following modules:
[0107] The distributed optical fiber vibration detection module is used to detect the signal to be measured generated by the signal source along the sensing optical fiber.
[0108] The signal source positioning module includes a multi-signal source identification unit and a mobile signal source identification unit. The multi-signal source identification unit is used to decompose the frequency energy components of the signal to be tested, and determine whether the signal to be tested belongs to a single source or multiple sources according to the frequency energy components of the signal to be tested. The mobile signal source identification unit is used to perform spatiotemporal dynamic analysis of the signal to be tested by using a sliding window indexing technique, and determine whether the signal to be tested belongs to a mobile source or a fixed source according to the spatiotemporal distribution characteristics of the signal to be tested.
[0109] The signal feature extraction and analysis module includes a single signal fixed source feature analysis unit, a multi-signal fixed source feature analysis unit, a single signal mobile source feature analysis unit, and a multi-signal mobile source feature analysis unit. The signal feature extraction and analysis module uses different analysis units to perform multi-domain feature analysis on the signal to be tested according to the type of the signal to be tested, and obtains a multi-dimensional feature vector corresponding to the signal to be tested. The types of the signals to be tested include a single signal fixed source, a single signal mobile source, a multi-signal fixed source, and a multi-signal mobile source.
[0110] The signal source pattern recognition module is used to track the spatiotemporal motion trajectory of each signal source according to the spatiotemporal distribution characteristics when the signal to be tested belongs to a mobile source. When the signal to be tested belongs to a fixed source, the spatial position of each signal source is located according to the spatiotemporal distribution characteristics. The module is also used to select different recognition units according to the type of the signal to be tested. The sub-recognition unit includes a single signal source recognition unit and a multi-signal source recognition unit. After selecting the recognition unit, the recognition unit performs pattern recognition on the signal to be tested through the multi-dimensional feature vector corresponding to the signal to be tested to obtain the event type corresponding to the signal to be tested.
[0111] Specifically, before performing pattern recognition, the multi-signal source identification unit needs to analyze the parts of the signal to be tested that have similar or overlapping frequency energies, obtain a set of time domain segment signals corresponding to the signal to be tested based on each fixed single frequency energy component, mark the aliased signals therein, and separate the components with similar frequencies in each aliased signal.
[0112] In this embodiment, please refer to Figure 4 , Figure 4 This is a schematic diagram of the operation flow of the signal source positioning module in the embodiment of the present application. The specific operation flow of the signal source positioning module includes the following steps:
[0113] Step A01: Set the width of the time domain sliding window to h, the interval to τ, and the number to n.
[0114] Step A02: Set the width of the spatial domain sliding window to d, the interval to l, and the number to .
[0115] Step A03: Select a spatial domain sliding window in the spatial domain and slide it along the time domain to obtain n time series segments.
[0116] Step A04: Demodulate the n time sequence segments to obtain the time domain waveform and spectrum of the signal to be measured.
[0117] Step A05: Determine whether the signal to be tested is a vibration signal. If so, proceed to step A06; if not, proceed to step A07.
[0118] Step A06: record the spatial position information of the signal to be tested and save the corresponding time sequence segment.
[0119] Step A07: Determine whether the current spatial domain sliding window is the last spatial domain window. If so, proceed to step A08; if not, return to step A04.
[0120] Step A08: Calculate the number of signal sources according to the spatial position information of the signal to be tested, and determine whether the signal source is a mobile signal source.
[0121] In this embodiment, please refer to Figure 5 , Figure 5 The following is a schematic diagram of the operation flow of the signal feature extraction and analysis module and the signal source pattern recognition module in the embodiment of the present application. The operation flow of the signal feature extraction and analysis module and the signal source pattern recognition module includes the following steps:
[0122] Step B01: Determine the type of the signal to be tested according to the spatial position information of the signal to be tested and the corresponding signal data. If the signal to be tested is a single signal fixed source, proceed to step B02; if the signal to be tested is a single signal mobile source, proceed to step B03; if the signal to be tested is a multi-signal fixed source, proceed to step B04; if the signal to be tested is a multi-signal mobile source, proceed to step B05.
[0123] Step B02: Perform single signal fixed source feature analysis and extraction on the signal to be tested to obtain a multi-dimensional feature vector corresponding to the signal to be tested, and then proceed to step B06.
[0124] Step B03: Perform single signal moving source feature analysis and extraction on the signal to be tested to obtain a multi-dimensional feature vector corresponding to the signal to be tested, and then proceed to step B06.
[0125] Step B04: Perform multi-signal fixed source feature analysis and extraction on the signal to be tested to obtain a multi-dimensional feature vector corresponding to the signal to be tested, and then proceed to step B07.
[0126] Step B05: Perform multi-signal moving source feature analysis and extraction on the signal to be tested to obtain a multi-dimensional feature vector corresponding to the signal to be tested, and then proceed to step B07.
[0127] Step B06: Perform single source signal pattern recognition on the signal to be tested according to the multidimensional feature vector corresponding to the signal to be tested to obtain the event type corresponding to the signal to be tested. If the signal source is a fixed source, proceed to step B09; if the signal to be tested is a mobile source, proceed to step B10.
[0128] Step B07: Perform pattern classification on the signal to be tested.
[0129] Step B08: Separate the aliased signal in the signal to be tested. If the signal to be tested belongs to a fixed source, proceed to step B11; if the signal to be tested belongs to a mobile source, proceed to step B12.
[0130] Step B09: spatially locate the signal source corresponding to the signal to be measured, and obtain the spatial position of the signal source corresponding to the signal to be measured.
[0131] Step B10: Tracking the time-space trajectory of the signal source corresponding to the signal to be measured.
[0132] Step B11: spatially locate each signal source corresponding to the signal to be measured.
[0133] Step B12: Tracking the time-space trajectory of each signal source corresponding to the signal to be measured.
[0134] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.
[0135] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.
[0136] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.
[0137] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.
[0138] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.
[0139] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.
[0140] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A method for two-dimensional signal source trajectory tracking and multi-source signal separation, characterized in that: The method comprises: Construct a three-dimensional space-time model of the backscattered Rayleigh scattering corresponding to the signal to be tested, and perform space-time dynamic analysis through sliding window indexing technology. If the signal to be tested belongs to a mobile source, track the space-time motion trajectory of each signal source according to the space-time distribution characteristics; The frequency energy components of the signal to be tested are decomposed. If the signal to be tested belongs to multiple sources, the parts with similar or overlapping frequency energies in the signal to be tested are analyzed. According to each fixed single frequency energy component, the time domain segment signal set corresponding to the signal to be tested is obtained, and the aliased signals therein are marked to separate the components with similar frequencies in each aliased signal.
2. The method for two-dimensional signal source trajectory tracking and multi-source signal separation according to claim 1, characterized in that: The step of constructing a three-dimensional space-time model of Rayleigh backward scattering corresponding to the signal to be measured includes: Obtaining a first measurement duration according to the optical cable detection distance, the number of detection light pulses, and the detection light pulse frequency corresponding to the signal to be measured; According to the Rayleigh backscattering curve corresponding to the signal to be measured within the first measurement duration, a three-dimensional space-time Rayleigh backscattering model corresponding to the signal to be measured is constructed in a temporal and spatial order.
3. The signal source two-dimensional trajectory tracking and multi-source signal separation method according to claim 1, characterized in that: The spatiotemporal dynamic analysis using the sliding window indexing technology includes: Constructing a time domain sliding window and a space domain sliding window respectively along the time axis and the space distance axis of the backward Rayleigh scattering space-time three-dimensional model; Slide each spatial domain sliding window along the time axis of the backward Rayleigh scattering space-time three-dimensional model with a preset step length to obtain a plurality of time series segments; Perform time domain differential demodulation on each time series segment to determine whether there is a vibration signal at the spatial position corresponding to the time series segment; All time series segments with vibration signals are inverted in time and space order to obtain the time and space distribution characteristics of the signal to be measured.
4. The method for two-dimensional signal source trajectory tracking and multi-source signal separation according to claim 1, characterized in that: Tracking the spatiotemporal motion trajectory of each signal source according to the spatiotemporal distribution characteristics includes: Invert the spatiotemporal distribution characteristics in time and space order to generate a spatiotemporal distribution diagram corresponding to the signal to be measured; In the time-space distribution diagram corresponding to the signal to be measured, the time-space motion trajectory of each signal source is obtained by fitting through the least square fitting method.
5. The signal source two-dimensional trajectory tracking and multi-source signal separation method according to claim 1, characterized in that: The decomposing the frequency energy components of the signal to be measured comprises: The frequency energy components of the signal to be measured are decomposed by multi-scale wavelet packet decomposition technology to obtain the energy frequency components contained in the signal to be measured; If the signal to be measured contains only a single energy frequency component, the signal to be measured belongs to a single source; If the signal to be measured contains multiple energy frequency components, the signal to be measured belongs to multiple sources.
6. The method for two-dimensional signal source trajectory tracking and multi-source signal separation according to claim 1, characterized in that: The analyzing of the parts of the signal to be tested with similar or overlapping frequency energies, obtaining a time domain segment signal set corresponding to the signal to be tested according to each fixed single frequency energy component, marking the aliased signals therein, and separating the components with similar frequencies in each aliased signal, includes: By using short-time Fourier transform to analyze the parts of the measured signal with similar or overlapping frequency energies, an inverse Fourier transform is performed on each fixed single frequency energy component to obtain a time domain segment signal set corresponding to the measured signal; Each time domain segment signal in the time domain segment signal set is sequentially subjected to time domain cross-correlation calculation with the signal to be tested, and the time domain segment signals whose calculation results are lower than the preset cross-correlation coefficient threshold are marked as aliasing signals. The components with similar frequencies in each aliasing signal are separated by spectral subtraction, and the separated aliasing signals are amplified by time domain integration.
7. The method for two-dimensional signal source trajectory tracking and multi-source signal separation according to claim 1, characterized in that: After decomposing the frequency energy components of the measured signal, it also includes: Performing multi-domain feature analysis on the signal to be tested according to the type of the signal to be tested, obtaining a multi-dimensional feature vector corresponding to the signal to be tested; the type of the signal to be tested includes a single signal fixed source, a single signal mobile source, multiple signal fixed sources and multiple signal mobile sources; Pattern recognition is performed on the signal to be tested according to the multidimensional feature vector corresponding to the signal to be tested to obtain the event type corresponding to the signal to be tested.
8. The method for two-dimensional signal source trajectory tracking and multi-source signal separation according to claim 7, characterized in that: Performing multi-domain feature analysis on the signal to be tested according to the type of the signal to be tested, including: According to the type of the signal to be measured, feature analysis is performed in the time domain, frequency domain, time-space domain, time-frequency domain and wavelet domain of the signal to be measured.
9. The method for two-dimensional signal source trajectory tracking and multi-source signal separation according to claim 7, characterized in that: After obtaining the multi-dimensional feature vector corresponding to the signal to be tested, it also includes: The multidimensional feature vector corresponding to the signal to be measured is reduced in dimension by the principal component analysis method to remove the redundancy in the multidimensional feature vector corresponding to the signal to be measured.
10. A signal source two-dimensional trajectory tracking and multi-source signal separation system based on the method according to any one of claims 1 to 9, characterized in that: The system comprises: A construction module, used to construct a three-dimensional space-time model of backward Rayleigh scattering corresponding to the signal to be measured; The positioning module is used to perform spatiotemporal dynamic analysis on the signal to be measured by using the sliding window indexing technology. If the signal to be measured belongs to a mobile source, the spatiotemporal motion trajectory of each signal source is tracked according to the spatiotemporal distribution characteristics; The separation module is used to decompose the frequency energy components of the signal to be tested. If the signal to be tested belongs to multiple sources, the parts with similar or overlapping frequency energies in the signal to be tested are analyzed. According to each fixed single frequency energy component, a time domain segment signal set corresponding to the signal to be tested is obtained, and the aliased signals therein are marked to separate the components with similar frequencies in each aliased signal.