A mobile source positioning method, device, terminal and storage medium

Abstract

The mobile source positioning method, device, terminal and storage medium provided by the application, the mobile source positioning method comprises: acquiring original signals received by each vibration sensing array in a multi-node vibration sensing array, and determining a to-be-processed signal based on the original signals; a plurality of vibration monitoring devices are arranged in a planar array in each vibration sensing array; based on the to-be-processed signals received by each vibration sensing array, beamforming analysis is performed on each vibration sensing array respectively to determine a potential direction of a mobile signal source, and a candidate positioning point is determined based on the potential direction of the mobile signal source; and a target positioning point of the mobile signal source is determined from the candidate positioning point based on an energy distribution matrix constructed. The deployment mode of the multi-node vibration sensing array can be not limited by various complex weather, all-weather continuous monitoring can be realized, and accurate positioning of the mobile source can be realized through beamforming analysis.

Description

Technical Field

[0001] This invention relates to the field of positioning technology, and in particular to a mobile source positioning method, device, terminal, and storage medium. Background Technology

[0002] With the rapid development of the social economy and the continuous advancement of urbanization, traditional security methods are no longer able to meet the needs of modern security protection. In particular, when dealing with complex terrain conditions and extreme weather conditions, the monitoring limitations of traditional security methods are becoming increasingly prominent.

[0003] Currently, mainstream perimeter security technologies are mainly divided into two categories: one is traditional optical and electronic sensing technologies, represented by infrared beams and video surveillance. Although these technologies are widely used, their protection level is low, making them easy for intentional intruders to circumvent, and their monitoring effectiveness is significantly reduced due to changes in terrain complexity and weather conditions. The other category is cable sensing technology, mainly based on leaky cables and vibrating cables. This technology detects intrusion behavior by detecting changes in the physical characteristics of the cable, but it still has inherent drawbacks such as high power consumption, high maintenance costs, and limited monitoring distance.

[0004] Therefore, existing technologies have shortcomings and need to be improved and developed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a mobile source positioning method, device, terminal and storage medium that can achieve continuous monitoring around the clock without being affected by various complex weather conditions, thereby achieving accurate positioning of mobile sources.

[0006] The technical solution adopted by this invention to solve the technical problem is as follows:

[0007] A method for locating a mobile source, wherein the method includes:

[0008] The raw signals received by each vibration sensor array in the multi-node vibration sensor array are acquired, and the signal to be processed is determined based on the raw signals; wherein, multiple vibration monitoring devices are deployed in a planar array distribution in each vibration sensor array;

[0009] Based on the signal to be processed received by each vibration sensing array, a beamforming analysis is performed on each vibration sensing array to determine the potential direction of the moving signal source, and a candidate location point of the moving signal source is determined based on the potential direction of the moving signal source.

[0010] Construct the energy distribution matrix corresponding to the multi-node vibration sensing array, and determine the target location point of the moving signal source from the candidate location points based on the energy distribution matrix.

[0011] In one implementation, determining the signal to be processed based on the original signal includes:

[0012] The original signal is identified as the signal to be processed;

[0013] Alternatively, the target signal can be extracted from the original signal to obtain the signal to be processed corresponding to each vibration sensing array.

[0014] In one implementation, the step of extracting the target signal from the original signal to obtain the signal to be processed corresponding to each vibration sensing array includes:

[0015] The original signal is low-pass filtered to extract the target signal, thus obtaining the signal to be processed for each vibration sensing array.

[0016] Alternatively, wavelet transform processing can be performed on the original signal to extract the target signal, thus obtaining the signal to be processed corresponding to each vibration sensing array.

[0017] In one implementation, after determining the signal to be processed based on the original signal, the method further includes:

[0018] The signal to be processed is segmented according to a sliding time window with a preset time window length and a preset overlap rate to obtain a processed signal corresponding to each vibration sensing array, which contains multiple overlapping vibration signal segments.

[0019] In one implementation, the distance between each vibration monitoring device in each vibration sensing array is no greater than half the wavelength of the original signal.

[0020] In one implementation, the planar array is any one of a planar square array, a planar circular array, or a planar hexagonal array.

[0021] In one implementation, the step of performing beamforming analysis on each vibration sensing array based on the signal to be processed received by each vibration sensing array to determine the potential direction of the moving signal source includes:

[0022] For each signal incident direction, the arrival time of the signal to be processed corresponding to each vibration monitoring device is aligned based on the calculated theoretical arrival time difference of each vibration monitoring device in each vibration sensing array, and the time-aligned signals are superimposed to obtain the superimposed signal corresponding to each signal incident direction under each vibration sensing array.

[0023] The waveform focusing energy corresponding to each signal incident direction under each vibration sensor array is determined based on the superimposed signal corresponding to each signal incident direction under each vibration sensor array.

[0024] The potential direction of the moving signal source corresponding to each vibration sensor array is determined based on the waveform beamforming energy corresponding to the incident direction of each signal under each vibration sensor array.

[0025] In one implementation, determining the potential direction of the moving signal source corresponding to each vibration sensing array based on the waveform beamforming energy corresponding to each signal incident direction under each vibration sensing array includes:

[0026] Determine whether the waveform focusing energy corresponding to the incident direction of each signal under each vibration sensor array exceeds a preset energy threshold.

[0027] When the waveform beam energy corresponding to the signal incident direction exceeds the preset energy threshold, the signal incident direction is determined to be the potential direction of the moving signal source, and the potential direction of the moving signal source corresponding to each vibration sensing array is obtained.

[0028] In one implementation, determining the candidate location point of the mobile signal source based on the potential direction of the mobile signal source includes:

[0029] The potential directions of the mobile signal source are intersected pairwise to determine the corresponding intersection points;

[0030] The energy confidence level of the intersection point is determined based on the waveform beam-gathering energy corresponding to the potential direction of the mobile signal source.

[0031] The distance confidence level of the intersection point is determined based on the distance between the intersection point and each of the vibration sensor arrays.

[0032] The target confidence level of the intersection point is determined based on the energy confidence level and the distance confidence level;

[0033] Determine whether the target confidence level of the intersection point is greater than a preset confidence threshold;

[0034] If the target confidence level at the intersection point is greater than the preset confidence threshold, then the intersection point is determined as a candidate location point for the mobile signal source.

[0035] In one implementation, the superposition of the time-aligned signals includes:

[0036] The time-aligned signals corresponding to each vibration monitoring device in each vibration sensing array are superimposed using either linear or nonlinear superposition methods.

[0037] In one implementation, constructing the energy distribution matrix corresponding to the multi-node vibration sensing array includes:

[0038] A corresponding two-dimensional grid is established based on the multi-node vibration sensing array, and the joint energy corresponding to each grid point in the two-dimensional grid is calculated.

[0039] A corresponding energy distribution matrix is ​​constructed based on the joint energy corresponding to all the grid points.

[0040] In one implementation, calculating the joint energy corresponding to each grid point in the two-dimensional grid includes:

[0041] For each grid point in the two-dimensional grid, calculate the azimuth angle of each vibration sensor array at the grid point, and determine the superimposed signal obtained by beamforming analysis of each vibration sensor array at the azimuth angle.

[0042] Perform a Hilbert transform on the superimposed signal to obtain the envelope signal;

[0043] Based on the distance between the grid points and the vibration sensing array, the envelope signal is absolutely time-corrected to obtain the time-corrected envelope signal.

[0044] Determine the target time-corrected envelope signal corresponding to the target vibration sensing array with the largest energy, and determine the peak value of the target time-corrected envelope signal;

[0045] Based on the peak value, a corresponding cutoff interval is determined, and the time-corrected envelope signal corresponding to each vibration sensing array within the cutoff interval is extracted to calculate the joint energy corresponding to the grid point.

[0046] In one implementation, determining the target location of the mobile signal source from the candidate location points based on the energy distribution matrix includes:

[0047] Based on the distance between the candidate location points, and using a clustering algorithm to perform cluster analysis on the candidate location points, a set of candidate location points corresponding to each cluster is obtained;

[0048] The coordinates of the candidate location points in the candidate location point set corresponding to each cluster are averaged to obtain the target candidate location point corresponding to each cluster.

[0049] Determine whether the target candidate location point corresponding to each cluster is located within the maximum value range of the energy distribution matrix;

[0050] If the target candidate location point is located within the range of the maximum value of the energy distribution matrix, then the target candidate location point is determined to be the target location point.

[0051] In one implementation, after averaging the coordinates of the candidate location points in the candidate location point set corresponding to each cluster to obtain the target candidate location points for each cluster, the method further includes:

[0052] Based on the target candidate positioning point, the corresponding angle correction value is determined from the pre-constructed angle correction field, and the angle correction value is used to correct the potential direction of the moving signal source corresponding to each vibration sensor array to obtain the corrected potential direction of the moving signal source.

[0053] New target candidate positioning points are determined based on the corrected potential direction of the moving signal source.

[0054] Record the current iteration count in real time and determine whether the current iteration count meets the preset iteration count;

[0055] If the current iteration count does not meet the preset iteration count, then based on the new target candidate positioning point, continue to execute the step of determining the corresponding angle correction value from the pre-constructed angle correction field and its subsequent steps;

[0056] When the current iteration count meets the preset iteration count, the new target candidate location point is determined as the valid target candidate location point of the mobile signal source.

[0057] In one implementation, the mobile source localization method further includes:

[0058] In the pre-set hammer impact experiment, the original hammer impact signal received by each vibration sensor array is processed based on the natural neighbor interpolation method to obtain the processed hammer impact signal.

[0059] The processed hammer impact signal received by each vibration sensor array is subjected to beamforming analysis to search for the direction of the target signal with the strongest energy corresponding to each vibration sensor array.

[0060] The target signal direction is compared with the actual signal direction at the impact point to calculate the angle deviation corresponding to each vibration sensing array, thereby obtaining the angle correction field corresponding to the multi-node vibration sensing array.

[0061] The present invention also discloses a mobile source positioning device, wherein the device comprises:

[0062] The signal acquisition module is used to acquire the raw signals received by each vibration sensor array in the multi-node vibration sensor array, and determine the signal to be processed based on the raw signals; wherein, each vibration sensor array has multiple vibration monitoring devices deployed in a planar array distribution.

[0063] A potential direction determination module is used to perform beamforming analysis on each vibration sensing array based on the signal to be processed received by each vibration sensing array, so as to determine the potential direction of the moving signal source.

[0064] A candidate location point determination module is used to determine candidate location points of the mobile signal source based on the potential direction of the mobile signal source;

[0065] A matrix construction module is used to construct the energy distribution matrix corresponding to the multi-node vibration sensing array;

[0066] The target location point determination module is used to determine the target location point of the mobile signal source from the candidate location points based on the energy distribution matrix.

[0067] The present invention also discloses a terminal, comprising: a memory, a processor, and a mobile source location program stored in the memory and executable on the processor, wherein the mobile source location program, when executed by the processor, implements the steps of the mobile source location method as described above.

[0068] The present invention also discloses a computer-readable storage medium storing a computer program that can be executed to implement the steps of the mobile source localization method as described above.

[0069] This invention provides a method, apparatus, terminal, and storage medium for locating a mobile source. The method includes: acquiring raw signals received by each vibration sensor array in a multi-node vibration sensor array, and determining a signal to be processed based on the raw signals; wherein multiple vibration monitoring devices are deployed in a planar array distribution in each vibration sensor array; performing beamforming analysis on each vibration sensor array based on the signal to be processed received by each vibration sensor array to determine the potential direction of the mobile signal source, and determining candidate location points of the mobile signal source based on the potential direction of the mobile signal source; constructing an energy distribution matrix corresponding to the multi-node vibration sensor array, and determining the target location point of the mobile signal source from the candidate location points based on the energy distribution matrix. Therefore, it can be seen that the deployment method of the multi-node vibration sensing array of the present invention can achieve continuous monitoring around the clock without being limited by the influence of various complex weather conditions. This allows the continuous raw signals received by each vibration sensing array to be acquired, and then the potential direction of the moving signal source can be determined by beamforming analysis to identify candidate positioning points. Finally, based on the constructed energy distribution matrix, the target positioning point of the moving signal source can be determined from the candidate positioning points, thus achieving accurate positioning of the moving source. Attached Figure Description

[0070] Figure 1 This is a flowchart of a preferred embodiment of the mobile source localization method in this invention;

[0071] Figure 2 This is a schematic diagram of the distribution of a specific vibration monitoring device disclosed in this invention;

[0072] Figure 3 This is a schematic diagram of a specific bundle analysis time-lapse correction disclosed in this invention;

[0073] Figure 4 This is a comparative schematic diagram of a specific nonlinear superposition and linear superposition disclosed in this invention;

[0074] Figure 5 This is a schematic diagram of a specific dual-station positioning method disclosed in this invention;

[0075] Figure 6 This is a schematic diagram of a specific station angle correction distribution disclosed in this invention;

[0076] Figure 7 This is a flowchart of a specific method for setting up instruments at an experimental site, as disclosed in this invention.

[0077] Figure 8 This is a schematic diagram of a specific signal source for array positioning disclosed in this invention;

[0078] Figure 9 This is a specific waveform separation diagram disclosed in this invention;

[0079] Figure 10 This is a flowchart of a specific mobile source localization method disclosed in this invention;

[0080] Figure 11 This is a schematic diagram of a specific real-time tracking of a mobile source disclosed in this invention;

[0081] Figure 12 This is a specific flowchart of a mobile source localization process disclosed in this invention;

[0082] Figure 13 This is a functional principle block diagram of a preferred embodiment of the mobile source positioning device in this invention;

[0083] Figure 14 This is a functional principle block diagram of a preferred embodiment of the terminal in this invention. Detailed Implementation

[0084] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0085] Please see Figure 1 , Figure 1This is a flowchart of the mobile source localization method in this invention. For example... Figure 1 As shown, the mobile source localization method described in this embodiment of the invention includes:

[0086] Step S11: Obtain the original signal received by each vibration sensor array in the multi-node vibration sensor array, and determine the signal to be processed based on the original signal; wherein, each vibration sensor array has multiple vibration monitoring devices deployed in a planar array distribution.

[0087] It should be noted that in actual monitoring sites, multiple vibration sensor arrays, i.e., multi-node vibration sensor arrays, are distributed. The layout of these arrays can be optimized according to site characteristics. A planar array layout can improve azimuth resolution. In other words, each vibration sensor array contains multiple vibration monitoring devices deployed in a planar array configuration. The spacing between devices in each array directly affects the array's spatial resolution and anti-aliasing performance. Therefore, to avoid spatial aliasing, the spacing between vibration monitoring devices in each array should not exceed half the wavelength of the original signal, i.e., d ≤ λ / 2. Simultaneously, the spacing should not be too small; otherwise, the received signal waveforms will be too similar, reducing the array's spatial resolution. For example, if the average propagation speed at the monitoring site is v = 250 m / s and the target signal's dominant frequency is f = 25 Hz, then the wavelength λ = v / f = 10 m, and the device spacing d = 3 m, satisfying the condition of being less than half the wavelength. Each vibration sensor array consists of N vibration monitoring devices, and the number of devices depends on the intensity of the background noise at the application site. The more vibration monitoring devices there are, the stronger the noise resistance; generally, N should be no less than 9.

[0088] In the application of beamforming analysis technology, the geometric distribution of the array has a decisive impact on the spatial resolution and positioning accuracy of the signal. Irregular planar arrays can be deployed according to site conditions. As long as the array aperture and the number of nodes are sufficient, the positioning and tracking performance will not be affected. For example, the planar array can be any of the following: square, circular, or hexagonal. That is, any of these three arrays can be used as the distribution method for vibration monitoring equipment. Among them, circular arrays have more balanced resolution in all-round monitoring, which can effectively reduce monitoring blind spots; hexagonal deployment can optimize the resolution in different directions while maintaining high coverage density, improving the ability to capture signals in specific directions, and is suitable for scenarios with key monitoring needs for directional signals. The deployment of multi-node vibration sensor arrays can achieve continuous monitoring around the clock, regardless of various complex weather conditions, thereby acquiring the continuous raw signals received by each vibration sensor array.

[0089] In this embodiment, a concealed planar array deployment is employed. This involves burying multi-node vibration monitoring devices, such as multi-node seismographs (i.e., multi-node seismic stations), underground. These multi-node seismic stations are distributed in a planar array to form a seismic monitoring array. Multiple seismic monitoring arrays are deployed on the actual monitoring site, forming a multi-node seismic monitoring array corresponding to that site. The spacing between the seismographs is controlled within half the signal wavelength, effectively solving the problem of poor terrain adaptability of traditional technologies. In other words, by burying the nodal seismographs underground, truly concealed and traceless monitoring is achieved, significantly improving the concealment of the protection system. Furthermore, nodal seismographs are not affected by various complex weather conditions such as heavy rain and fog, enabling continuous 24-hour monitoring. Moreover, the long service life of nodal seismographs greatly reduces long-term maintenance costs.

[0090] In this embodiment, when the actual monitoring site is located, the multi-node vibration sensor array deployed on the site can receive raw signals containing various types of vibrations. That is, vibrations occur within the monitoring range of the vibration sensor array, and each vibration sensor array can receive the raw signal generated by the vibration. Thus, the raw signal received by each vibration sensor array in the multi-node vibration sensor array is obtained, and then the signal to be processed is determined based on the raw signal.

[0091] In one specific implementation, determining the signal to be processed based on the original signal may specifically include: identifying the original signal as the signal to be processed. That is, directly using the vibration sensing array to receive the original signal and apply it to the data in subsequent processing steps.

[0092] In another specific implementation, the signal to be processed is determined based on the original signal. Specifically, this may include extracting the target signal from the original signal to obtain the signal to be processed corresponding to each vibration sensing array. It is understood that preprocessing the original signals acquired by vibration monitoring equipment, such as nodal seismographs, for example, by low-pass filtering, can improve signal quality.

[0093] It should be noted that various environmental noises are recorded during actual data acquisition, including vibrations caused by the operation of fixed equipment within the monitoring site and vibrations caused by aerial transportation equipment such as airplanes and helicopters. Therefore, the raw signal received by each vibration monitoring device in the vibration sensor array includes both the target signal and noise. In the scenario of identifying minute vibration signals, the main target signals are vibrations caused by human footsteps and various vehicles, with the main frequency band of these signals being 0 to 30 Hz. Therefore, low-pass filtering is performed as a preprocessing step to improve signal quality.

[0094] Furthermore, the target signal can be extracted from the original signal through low-pass filtering or wavelet transform. Specifically, the original signal can be low-pass filtered to extract the target signal, yielding the signal to be processed for each vibration sensor array; or, the original signal can be wavelet transformed to extract the target signal, yielding the signal to be processed for each vibration sensor array. Wavelet transform, with its multi-resolution analysis capabilities, enables more refined time-frequency analysis of the signal and can adapt to more complex frequency band characteristics. It is particularly advantageous when processing non-stationary signals, as it can more accurately extract the effective components of the signal, improving the accuracy and reliability of signal processing.

[0095] Step S12: Based on the signal to be processed received by each vibration sensing array, perform beamforming analysis on each vibration sensing array to determine the potential direction of the moving signal source, and determine the candidate location point of the moving signal source based on the potential direction of the moving signal source.

[0096] In this embodiment, beamforming analysis is used to analyze the signal to be processed in order to search for potential directional information of the moving signal source. Specifically, by beamforming the raw data, the signal energy distribution in different directions is calculated, and the direction corresponding to the energy peak is identified, thereby determining the potential direction of the moving signal source. Then, based on the potential direction of the moving signal source, candidate location points are determined, enabling accurate positioning of the moving signal source in subsequent processing. The advantages of beamforming analysis make this application show broad application prospects in the field of perimeter security. Alternatively, machine learning models, such as CNNs, can be used to replace beamforming analysis. CNNs, trained on large amounts of data, can automatically learn the directional features of signals, possessing powerful feature extraction and pattern recognition capabilities. They do not require manually setting complex parameters, enabling more accurate positioning in complex environments, and their positioning performance can be continuously optimized as the amount of data increases.

[0097] It should be noted that the clustering analysis method is based on the plane wave incident assumption, that is, it assumes that the moving signal source is located in the far field, and its wavefront can be approximated as a plane wave. To satisfy this assumption and maximize the array performance, see [reference needed]. Figure 2 As shown, the monitoring stations (vibration monitoring equipment) can be evenly distributed in a planar square array on a two-dimensional plane. Sixteen stations are arranged in a 4×4 square, with a station spacing (equipment spacing) of 3 meters. The solid line represents the direction of the moving signal source incident on the station, satisfying the plane wave assumption, while the dashed line represents the wavefront of the incident wave. This distribution method effectively captures the spatial information of the signal in the horizontal direction.

[0098] In this embodiment, based on the signal to be processed received by each vibration sensing array, beamforming analysis is performed on each vibration sensing array to determine the potential direction of the moving signal source. Specifically, this may include: for each signal incident direction, aligning the signals to be processed corresponding to each vibration monitoring device based on the calculated theoretical arrival time difference of each vibration monitoring device in each vibration sensing array, and superimposing the time-aligned signals to obtain the superimposed signal corresponding to each signal incident direction under each vibration sensing array; determining the waveform beamforming energy corresponding to each signal incident direction under each vibration sensing array based on the superimposed signal corresponding to each signal incident direction under each vibration sensing array; and determining the potential direction of the moving signal source corresponding to each vibration sensing array based on the waveform beamforming energy corresponding to each signal incident direction under each vibration sensing array. Understandably, beamforming analysis is a spatial filtering technique based on array signal processing. It enhances signals from a specific direction by aligning and superimposing waveform signals received by multiple vibration monitoring devices in a vibration sensing array, while suppressing noise from other directions. Its core idea is to utilize the time difference between the signal source and different vibration monitoring devices to enhance the signal energy in the target direction through coherent superposition.

[0099] The process involves superimposing the time-aligned signals, including using either linear or nonlinear superposition methods to superimpose the time-aligned signals corresponding to each vibration monitoring device in each vibration sensing array. The nonlinear superposition method significantly enhances the ability to identify weak signals and substantially reduces environmental noise interference.

[0100] Furthermore, the potential direction of the moving signal source corresponding to each vibration sensor array is determined based on the waveform beamgear energy corresponding to each signal incident direction under each vibration sensor array. Specifically, this may include: determining whether the waveform beamgear energy corresponding to each signal incident direction under each vibration sensor array exceeds a preset energy threshold; when the waveform beamgear energy corresponding to the signal incident direction exceeds the preset energy threshold, the signal incident direction is determined to be the potential direction of the moving signal source, thus obtaining the potential direction of the moving signal source corresponding to each vibration sensor array.

[0101] For example, in practical applications, the reciprocal of velocity is used to describe the field speed, i.e., the slowness:

[0102]

[0103] When the moving signal source is sufficiently far from the vibration sensing array, the signal wavefront can be approximated as a plane wave. Assuming the moving signal source is located in the far field, a surface wave propagating on the plane of Earth can be described by the azimuth angle θ of the incident direction; therefore, the slowness vector can be described as:

[0104]

[0105] When there are N vibration monitoring devices in each vibration sensing array, the position of the i-th vibration monitoring device is:

[0106] r i =(x i ,y i );

[0107] Taking the first vibration monitoring device as a reference, the time difference for the signal to reach the i-th vibration monitoring device is:

[0108] t i =(r i -r1)*s;

[0109] For an incident direction θ, calculate the theoretical arrival time difference t for each vibration monitoring device in the vibration sensing array. i (θ), the waveform signal u(t,θ) received by each vibration monitoring device is time-aligned to obtain the time-aligned waveform signal: u i ′ (t,θ)=u i (t+t i (θ),θ);

[0110] After alignment, the waveform signal includes the target signal waveform f(t,θ) and noise n. i (t,θ), that is:

[0111] u i ′ (t,θ)=f(t,θ)+n i (t,θ);

[0112] To further enhance the signal in the target direction and suppress noise, the time-aligned waveform signal is superimposed, i.e.:

[0113]

[0114] Where, noise n i (t,θ) is random, and the signals remain incoherent after alignment. Therefore, the noise is suppressed after superposition.

[0115] And see also Figure 3 As shown, before superimposing the arrival time aligned waveform signals, the arrival time aligned waveform signals can also be corrected. When the signal is incident from the due east direction of the array, the original signal waveform shows that there are obvious differences in arrival times between different stations. By correcting the arrival time waveform signals through beamforming analysis, the arrival times between different stations are consistent, so the superimposed waveform signal is clearer.

[0116] The above method uses linear superposition, while nonlinear superposition is more sensitive to anomalies and can improve signal resolution. Therefore, the superimposed waveform signal obtained by nonlinear superposition is y(t,θ), that is:

[0117]

[0118] Where R is the parameter of the nonlinear superposition, and see [reference needed]. Figure 4 As shown, the nonlinear superposition waveform is more sensitive to signal characteristics and significantly improves the signal-to-noise ratio.

[0119] By repeating the time alignment and superposition process above for different incident directions θ, the superimposed waveform signal y(t,θ) corresponding to each incident direction θ can be obtained, and the waveform beamforming energy E(θ) for each direction θ can be obtained, that is:

[0120] E(θ)=∫|y(t,θ)| 2 dt.

[0121] It should be noted that the waveform beam energy E(θ) reflects the signal intensity in different directions. By plotting the azimuth-energy relationship, the potential direction of the moving signal source can be observed intuitively. The direction corresponding to the peak energy is the potential direction of the moving signal source.

[0122] In this embodiment, determining candidate location points for a mobile signal source based on its potential directions may specifically include: intersecting each potential direction of the mobile signal source pairwise to determine corresponding intersection points; determining the energy confidence level of the intersection points based on the waveform beamforming energy corresponding to the potential directions of the mobile signal sources; determining the distance confidence level of the intersection points based on the distances between the intersection points and each vibration sensor array; determining the target confidence level of the intersection points based on the energy confidence level and the distance confidence level; determining whether the target confidence level of the intersection points is greater than a preset confidence threshold; and if the target confidence level of the intersection points is greater than the preset confidence threshold, then the intersection points are determined as candidate location points for the mobile signal source. It can be understood that preliminary candidate location points are determined by using the pairwise intersections of the potential directions of the mobile signal sources corresponding to each vibration sensor array.

[0123] For example, when four vibration sensor arrays are deployed at the actual monitoring site, cluster analysis is performed on each array to obtain the signal energy intensity of each array in different directions at that moment. When the signal energy in a certain direction exceeds a preset energy threshold, that direction is identified as a potential direction for a moving signal source, i.e., a potential direction of the moving signal source. The intersection points are calculated by pairwise combination of the potential directions of the moving signal source obtained from the four vibration monitoring devices. Based on the statistical characteristics of background noise, the preset energy threshold can be set to three times the standard deviation of the average noise energy.

[0124] See Figure 5 As shown, for dual-array (i.e., two vibration sensor arrays) localization, circles represent signals, and triangles represent arrays, with each array consisting of 16 stations. The signal moves from left to right, and the potential directions of the moving signal source obtained by the two arrays through beamforming analysis are intersected to achieve signal source localization and tracking.

[0125] With the two arrays located at x1 and x2 respectively, and the potential directions of the mobile signal sources they search for being θ1 and θ2, the intersection point p can be calculated through geometric relationships. Each intersection point is evaluated to determine its energy confidence and distance confidence. Then, based on the energy confidence and distance confidence, the target confidence of the intersection point is determined.

[0126] Among them, the energy confidence level C energy Defined as the normalized energy E of the two arrays in the searched direction. ′ The minimum value of (θ), that is:

[0127] C enerhy =min(E1) ′ (θ1),E2 ′ (θ2));

[0128] Distance confidence C distance The evaluation is performed by the distance between the intersection point and the array, such as at the intersection point p and the array x. i The distance is d i If the effective search range of the array is D, then the distance confidence level can be:

[0129]

[0130] Furthermore, when the intersection point p and the array x i distance d i When the distance is less than the length of the array, the intersection does not satisfy the plane wave assumption, and the distance confidence is set to 0.

[0131] Target confidence level C total , defined as the product of energy confidence and distance confidence:

[0132] C total =C energy *C distance ;

[0133] When the target confidence level reaches the preset confidence threshold C th If the intersection point is selected, then the intersection point is determined as a candidate location point for the mobile signal source.

[0134] In this embodiment, the distribution map of beam energy and azimuth angle is obtained by using the possible signal source directions through the beam analysis algorithm, and suitable candidate positioning points are calculated based on cross-positioning.

[0135] Step S13: Construct the energy distribution matrix corresponding to the multi-node vibration sensing array, and determine the target location point of the moving signal source from the candidate location points based on the energy distribution matrix.

[0136] In this embodiment, an energy distribution matrix is ​​constructed within the maximum signal search range of the multi-node vibration sensing array to determine the rationality of the preliminary candidate positioning points determined by the pairwise intersection of the potential directions of the moving signal sources in the aforementioned steps.

[0137] In this embodiment, constructing the energy distribution matrix corresponding to the multi-node vibration sensing array may specifically include: establishing a corresponding two-dimensional grid based on the multi-node vibration sensing array, and calculating the joint energy corresponding to each grid point in the two-dimensional grid; and constructing the corresponding energy distribution matrix based on the joint energy corresponding to all grid points.

[0138] The calculation of the joint energy corresponding to each grid point in the two-dimensional grid can specifically include: for each grid point in the two-dimensional grid, calculating the azimuth angle of each vibration sensor array under the grid point, and determining the superimposed signal obtained by the clustering analysis of each vibration sensor array under the azimuth angle; performing a Hilbert transform on the superimposed signal to obtain the envelope signal; performing absolute time-correction on the envelope signal according to the distance between the grid point and the vibration sensor array to obtain the time-corrected envelope signal; determining the target time-corrected envelope signal corresponding to the target vibration sensor array with the largest energy, and determining the peak value of the target time-corrected envelope signal; determining the corresponding interception interval based on the peak value, and extracting the time-corrected envelope signal corresponding to each vibration sensor array within the interception interval to calculate the joint energy corresponding to the grid point.

[0139] For example, the specific steps for constructing the energy distribution matrix are as follows:

[0140] Establish a two-dimensional mesh

[0141] Where, k i =(x i ,y i ) represents the coordinates of the i-th grid point.

[0142] For grid point k i Calculate the j-th array r j The azimuth angle θ is used to obtain the superimposed waveform y corresponding to the cluster analysis. j (t;θ);

[0143] For superimposed waveform y j Perform a Hilbert transform on (t; θ) to obtain the envelope of the waveform, i.e., the envelope signal:

[0144] Y j (t;θ)=∣yj (t;θ)+i·H{y j (t;θ)}∣;

[0145] Where H represents the Hilbert transform.

[0146] Then, based on the distance between the grid points and the array, the envelope signal is subjected to absolute time-delay correction:

[0147]

[0148] Where, τ j It is grid point k i up to the j-th array r j Absolute time;

[0149] Based on the vibration sensing array j with the highest energy * peak value of the envelope Obtain the corresponding cutoff interval, that is:

[0150] T = [t] peak -0.1,t peak +0.1];

[0151] in,

[0152] Furthermore, the vibration sensing array with the highest energy is:

[0153] j * =max(E j );

[0154] For each vibration sensing array j, extract the envelope within interval T. And calculate the joint energy corresponding to this grid point:

[0155]

[0156] Traverse all grid points k i The energy matrix is ​​obtained:

[0157]

[0158] Where m is the number of columns in the grid.

[0159] In this embodiment, determining the target location point of a moving signal source from candidate location points based on the energy distribution matrix can specifically include: clustering the candidate location points based on the distance between them using a clustering algorithm to obtain a set of candidate location points for each cluster; averaging the coordinates of the candidate location points in each cluster's set to obtain the target candidate location point for each cluster; determining whether the target candidate location point for each cluster is located within the maximum value range of the energy distribution matrix; if the target candidate location point is located within the maximum value range of the energy distribution matrix, then it is determined to be the target location point. It is understood that clustering analysis is performed on all candidate location points using a distance-based clustering algorithm; for example, if the distance between two intersection points is less than 10m, they can be grouped into one category. Averaging the coordinates of the candidate location points in each cluster yields the final valid point, i.e., the target candidate location point. When locating a moving signal source using multiple vibration monitoring devices and multiple search directions, false intersection points may occur due to the convergence of search angles caused by multiple moving signal sources. Therefore, the target location point is determined by constructing an energy distribution matrix covering the searchable area. If the candidate target location point is located within the range of local maxima of the energy distribution matrix, the candidate target location point is determined to be a reasonable intersection point, i.e., the target location point.

[0160] In this embodiment, after averaging the coordinates of the candidate positioning points in the candidate positioning point set corresponding to each cluster to obtain the target candidate positioning point for each cluster, the method may further include: determining the corresponding angle correction value from a pre-constructed angle correction field based on the target candidate positioning point, and using the angle correction value to correct the potential direction of the moving signal source corresponding to each vibration sensor array to obtain the corrected potential direction of the moving signal source; determining a new target candidate positioning point based on the corrected potential direction of the moving signal source; recording the current iteration number in real time and determining whether the current iteration number meets the preset iteration number; if the current iteration number does not meet the preset iteration number, then based on the new target candidate positioning point, continuing to execute the step of determining the corresponding angle correction value from the pre-constructed angle correction field and its subsequent steps; if the current iteration number meets the preset iteration number, then determining the new target candidate positioning point as a valid target candidate positioning point for the moving signal source.

[0161] For example, for each vibration sensing array, search for the grid point (x) closest to the effective point. g ,y g ), and obtain its corresponding angle correction value Δθ(x) g ,y g ), and the angle correction value Δθ(x) g ,y gThe search direction obtained by applying the vibration sensor array is used to correct the propagation direction of the correction signal. Based on the corrected signal propagation direction, the process of using multiple positioning signal sources is repeated to obtain new effective points. This process is repeated for five iterations to progressively optimize the position estimation of the effective points, ultimately yielding a corrected high-precision positioning result. (See also...) Figure 6 As shown, the left column of graphs displays the usable correction results after interpolation. Darker colors represent counter-clockwise correction, and lighter colors represent clockwise correction. In the right graph, circles represent GPS high-precision positioning, crosses represent the initial positioning results, and asterisks represent the positioning results after angle correction. The direction indicated by the vibration sensor array is the direction after angle correction. It can be seen that the result after angle correction is closer to GPS high-precision positioning.

[0162] It should be noted that due to the non-uniformity of the velocity structure of the experimental site, the signal is affected by velocity changes during propagation, causing deviations in the waveform and time difference recorded by the vibration monitoring equipment. To correct this error caused by the velocity structure, an angle correction term can be introduced, and the velocity correction of the site can be achieved through high-precision hammer impact experiments and interpolation methods. In other words, for waveform changes caused by abnormal site velocity structure, site velocity structure correction based on natural neighbor interpolation is used.

[0163] In the pre-set hammer impact experiment, the original hammer impact signal received by each vibration sensor array is processed based on the natural neighbor interpolation method to obtain the processed hammer impact signal; the processed hammer impact signal received by each vibration sensor array is subjected to cluster analysis to search for the direction of the target signal with the strongest energy corresponding to each vibration sensor array; the target signal direction is compared with the actual signal direction of the hammer impact point to calculate the angle deviation corresponding to each vibration sensor array, and the angle correction field corresponding to the multi-node vibration sensor array is obtained.

[0164] For example, see Figure 7 As shown, the detection range of the array was first tested under the monitoring site conditions. The array position was set according to the site conditions, such as a 4×4 array, and precise coordinates were recorded. The relevant instruments were powered on, and signals were recorded. A grid covering the monitoring site was constructed with 10-meter intervals. A hammer impact experiment was conducted using this grid as a reference, recording the precise position (x, y) and impact time t0 of each impact point. The impact waveforms were synchronously received and recorded via a vibration sensor array. Beamforming analysis was performed on the impact waveforms recorded by each vibration sensor array to search for the direction θ of the target signal with the strongest energy. max and the actual signal direction θ at the hammer impact point true By comparing, the angular deviation Δθ is calculated, i.e.:

[0165] Δθ=θ max -θ true ;

[0166] Using this method, the angle correction value Δθ(x,y) of each vibration sensor array within the monitoring site is obtained.

[0167] During the hammering experiment, the environment was kept quiet for 5 seconds before and after the hammering to avoid background noise interference with the signal, thereby obtaining a hammering signal with a high signal-to-noise ratio.

[0168] Furthermore, to obtain higher longitude angle correction, natural neighbor interpolation was performed on the original data, increasing the grid density to 1m×1m. By calculating the natural neighborhood relationship between the interpolation points and surrounding data points, the interpolation weights were determined, maintaining data smoothness while constructing a high-precision single-station angle correction field Δθ. high-res (x,y).

[0169] As can be seen, in this embodiment of the invention, the deployment of a multi-node vibration sensor array can achieve continuous monitoring around the clock, unaffected by various complex weather conditions. This allows the acquisition of continuous raw signals received by each vibration sensor array, followed by beamforming analysis to determine the potential direction of the moving signal source, identifying candidate location points, and finally determining the target location point of the moving signal source from the candidate location points based on the constructed energy distribution matrix, thus achieving precise positioning of the moving source.

[0170] For example, see Figure 8 As shown, the first column displays the waveform data from the stations. The gray data represents the waveform data from the 16 stations in the array, while the black data represents the superimposed waveform data. The horizontal axis represents time, and the vertical axis represents amplitude. The second column is a distribution diagram of the angle and energy relationship obtained from beamforming analysis, with the radius representing the energy magnitude. In the right-hand diagram, the triangles represent the array, the circles represent the actual signal location, the black dots represent the location located by the beamforming analysis method, and the black circles represent the searchable range of the four array beamforming analyses, i.e., the array positioning range. The diagram shows that the final positioning result is close to the actual record.

[0171] It should be noted that for scenarios with multiple moving sources coexisting, waveform signals can be extracted using signal separation algorithms. For example, in the localization problem under multiple signal source scenarios, cluster analysis can be used to separate the signal waveforms, and the aforementioned localization process can be iteratively executed to achieve the differentiation of multiple moving targets, thereby achieving the localization of multiple moving signal sources. For instance, when there are two moving signal sources in the scenario, the cluster analysis result will yield two main directions θ1 and θ2. Based on the largest peak direction θ1, the linearly superimposed waveform y1(t) of N stations in the array is calculated. Subtracting the superimposed waveform y1(t) with the largest peak from the original waveform y(t) yields the remaining waveform y2(t), which contains information about the peak direction of θ2, thus achieving effective signal separation. See also... Figure 9As shown, the top left corner shows the situation where two signal sources moved simultaneously, as recorded by field array 2. The top right corner shows the beam energy distribution obtained from beamforming analysis at that moment, revealing two distinct energy peaks. The bottom left corner shows the original waveforms of the data, where gray represents the waveforms of the 16 stations in array 2, and black represents the superimposed waveform of the maximum energy detected, i.e., the waveform of signal source 1. The waveform of signal source 2 is obtained by subtracting the waveform of signal source 1 from the original waveforms of the stations and then superimposing them.

[0172] See Figure 10 As shown, this embodiment of the invention discloses a specific method for locating mobile sources. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution.

[0173] Step S21: Obtain the original signal received by each vibration sensor array in the multi-node vibration sensor array, and determine the signal to be processed based on the original signal; wherein, each vibration sensor array has multiple vibration monitoring devices deployed in a planar array distribution.

[0174] Step S22: According to a sliding time window with a preset time window length and a preset overlap rate, the signal to be processed is segmented to obtain the processed signal corresponding to each vibration sensing array, which contains multiple overlapping vibration signal segments.

[0175] In this embodiment, for the real-time tracking requirement of multiple moving targets, the acquired signals need to be converted into short time segments. This requires selecting an appropriate time window, that is, using a sliding time window with a specific length and overlap rate to extract the signal from the continuously acquired waveform data. For example, a time window with a length of 1 second and an overlap rate of 50% can be used to divide the continuously acquired raw signal into multiple overlapping signal segments.

[0176] The shorter window length ensures rapid capture of dynamic changes in the target signal, while the 50% overlap rate prevents the omission of key information between signal segments, allowing for smooth data transitions between adjacent time windows. During multi-source signal separation and real-time tracking, continuous analysis of signal segments via a sliding time window enables timely detection of newly emerging moving targets, accurate tracking of target trajectories, and effectively improved real-time monitoring capabilities for multiple moving targets. This provides stable and continuous signal data support for subsequent positioning and early warning functions.

[0177] Step S23: Based on the signal to be processed received by each vibration sensing array, perform beamforming analysis on each vibration sensing array to determine the potential direction of the moving signal source, and determine the candidate location point of the moving signal source based on the potential direction of the moving signal source.

[0178] Step S24: Construct the energy distribution matrix corresponding to the multi-node vibration sensing array, and determine the target location point of the moving signal source from the candidate location points based on the energy distribution matrix.

[0179] For details regarding steps S21 and S23 to S24, please refer to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

[0180] It is understood that in this embodiment, continuously acquired waveform data is captured using a sliding time window with a specific length and overlap rate. Within each time window, the data undergoes preprocessing, and a beam energy and azimuth distribution map is obtained using a beamforming analysis algorithm targeting possible signal source directions. Suitable candidate positioning points are calculated based on cross-location, and these candidate points are judged according to the energy distribution matrix to obtain a reasonable positioning. Therefore, as the time window moves, real-time tracking of the moving source is achieved.

[0181] For example, see Figure 11 As shown, the image on the right is a schematic diagram of two arrays tracking a signal source in real time. The circles represent the signal moving from time 1 to time 3, and the direction searched by the arrays changes accordingly. The first column on the left shows the beam energy distribution of array 2 changing with time, and the second column shows the beam energy distribution of array 3 changing with time. The bottom left corner shows the waveform data of one of the stations in array 2.

[0182] As can be seen, in this embodiment, the deployment of a multi-node vibration sensor array can achieve continuous monitoring around the clock, unaffected by various complex weather conditions. This allows the acquisition of continuous raw signals received by each vibration sensor array, followed by beamforming analysis to determine the potential direction of the moving signal source, identify candidate location points, and finally determine the target location point of the moving signal source from the candidate location points based on the constructed energy distribution matrix, thus achieving precise positioning of the moving source.

[0183] For example, see Figure 12As shown, data preprocessing is performed on the collected actual signal data. For real-time tracking of multiple moving sources, the collected signals are converted into short signal segments. An appropriate time window needs to be selected, and then the signal segments are continuously analyzed using a sliding time window. This allows for the timely detection of newly emerging moving targets. Preliminary candidate points can be determined through cross-location using multiple arrays, and possible signal directions can be calculated through beamforming analysis. Specifically, in the hammer impact experiment, an angle correction term can be constructed based on the hammer impact signal, and this term can be used to correct the angle of the preliminary candidate points. Furthermore, when determining the final location, a regional energy spatial distribution can be constructed based on the beamforming energy distribution of the stations. It can be determined whether the candidate point is within the maximum range of the energy distribution matrix. If it is, the candidate point is determined as the final location point; otherwise, the false candidate point is removed.

[0184] It should be noted that the technical solution of this application can also be used in practical applications such as mine safety monitoring, wildlife tracking, and remote real-time monitoring in conjunction with 5G transmission.

[0185] In the complex underground environment of mines, multi-node seismograph arrays can be concealed and flexibly deployed in key areas such as roadways and goafs. Through high-precision signal acquisition and analysis, they can not only accurately locate underground workers and promptly grasp their distribution, but also monitor and analyze rock vibration signals in real time based on dynamic velocity correction methods and multi-source signal separation technology. This allows for the early detection of subtle vibration changes before a collapse, thus issuing collapse warnings and providing strong technical support for safe mine production, effectively reducing the probability of accidents and protecting the lives and property of personnel and enterprises. In natural ecological environments, multi-node seismograph arrays can be deployed in wildlife habitats. Utilizing the unique characteristics of surface vibration signals, nonlinear clustering analysis technology enhances the identification of weak vibration signals. Combined with multi-source signal separation and real-time tracking technology, the activity trajectories of different species of wild animals can be accurately identified. This non-invasive monitoring method, compared to traditional tracking methods such as GPS collars, causes less interference to wild animals, helping researchers to gain a more realistic and comprehensive understanding of the living habits and migration patterns of wild animals, providing important data support for wildlife conservation and ecological environment research. In remote real-time monitoring using 5G transmission, 5G technology, with its high speed, low latency, and large capacity, allows for the rapid and stable transmission of large amounts of seismic signal data collected by monitoring equipment to remote servers or monitoring centers via the 5G network. On one hand, this enables real-time sharing of monitoring data, allowing relevant personnel to obtain on-site monitoring information promptly. On the other hand, leveraging the powerful computing resources of the cloud, complex signal processing and analysis tasks originally performed locally can be transferred to the cloud, significantly reducing the computing power requirements of local devices, lowering equipment costs and energy consumption. It also provides broader scope for system functional expansion and upgrades, enabling the technical solution of this application to be applied more efficiently in more fields.

[0186] In one embodiment, such as Figure 13 As shown, based on the above-described mobile source positioning method, the present invention also provides a mobile source positioning device, comprising:

[0187] The signal acquisition module 11 is used to acquire the original signal received by each vibration sensor array in the multi-node vibration sensor array, and determine the signal to be processed based on the original signal; wherein, each vibration sensor array has multiple vibration monitoring devices deployed in a planar array distribution.

[0188] The potential direction determination module 12 is used to perform beamforming analysis on each vibration sensing array based on the signal to be processed received by each vibration sensing array, so as to determine the potential direction of the moving signal source.

[0189] The candidate location point determination module 13 is used to determine the candidate location points of the mobile signal source based on the potential direction of the mobile signal source;

[0190] Matrix construction module 14 is used to construct the energy distribution matrix corresponding to the multi-node vibration sensing array;

[0191] The target location point determination module 15 is used to determine the target location point of the mobile signal source from the candidate location points based on the energy distribution matrix.

[0192] In some specific embodiments, the signal acquisition module 11 may specifically include:

[0193] The first signal processing unit is used to determine the original signal as the signal to be processed;

[0194] Alternatively, a second signal processing unit is used to extract the target signal from the original signal to obtain the signal to be processed corresponding to each vibration sensing array.

[0195] In some specific embodiments, the second signal processing unit may specifically include:

[0196] The first extraction subunit is used to perform low-pass filtering on the original signal to extract the target signal, thereby obtaining the signal to be processed corresponding to each vibration sensing array.

[0197] Alternatively, the second extraction subunit is used to perform wavelet transform processing on the original signal to extract the target signal, thereby obtaining the signal to be processed corresponding to each vibration sensing array.

[0198] In some specific embodiments, the mobile source positioning device may further include:

[0199] The signal segmentation module is used to segment the signal to be processed according to a sliding time window with a preset time window length and a preset overlap rate, so as to obtain the processed signal corresponding to each vibration sensing array, which contains multiple overlapping vibration signal segments.

[0200] In some specific embodiments, the potential direction determination module 12 may specifically include:

[0201] The arrival time alignment unit is used to perform arrival time alignment on the signal to be processed corresponding to each vibration monitoring device for each signal incident direction, based on the calculated theoretical arrival time difference of each vibration monitoring device in each vibration sensing array.

[0202] The signal superposition unit is used to superimpose the time-aligned signals to obtain the superimposed signal corresponding to the incident direction of each signal under each vibration sensing array;

[0203] A waveform focusing energy determination unit is used to determine the waveform focusing energy corresponding to the incident direction of each signal under each vibration sensing array based on the superimposed signal corresponding to the incident direction of each signal under each vibration sensing array.

[0204] A potential direction determination unit is used to determine the potential direction of the moving signal source corresponding to each vibration sensing array based on the waveform beamforming energy corresponding to the incident direction of each signal under each vibration sensing array.

[0205] In some specific embodiments, the potential direction determination unit may specifically include:

[0206] The energy judgment subunit is used to determine whether the waveform beaming energy corresponding to the incident direction of each signal under each vibration sensing array exceeds a preset energy threshold.

[0207] The direction determination subunit is used to determine that the signal incident direction is the potential direction of the moving signal source when the waveform focusing energy corresponding to the signal incident direction exceeds the preset energy threshold, thereby obtaining the potential direction of the moving signal source corresponding to each vibration sensing array.

[0208] In some specific embodiments, the candidate location point determination module 13 may specifically include:

[0209] The direction intersection unit is used to intersect the potential directions of the mobile signal source pairwise to determine the corresponding intersection points;

[0210] An energy confidence determination unit is used to determine the energy confidence of the intersection point based on the waveform beaming energy corresponding to the potential direction of the mobile signal source;

[0211] A distance confidence determination unit is used to determine the distance confidence of the intersection point based on the distance between the intersection point and each of the vibration sensing arrays;

[0212] A target confidence determination unit is used to determine the target confidence of the intersection point based on the energy confidence and the distance confidence;

[0213] A confidence level determination unit is used to determine whether the target confidence level of the intersection point is greater than a preset confidence level threshold.

[0214] The candidate location point determination unit is used to determine the intersection point as a candidate location point of the mobile signal source if the target confidence level at the intersection point is greater than the preset confidence threshold.

[0215] In some specific embodiments, the signal superposition unit may specifically include:

[0216] The signal superposition subunit is used to superimpose the time-aligned signals corresponding to each vibration monitoring device in each vibration sensing array using a linear superposition method or a nonlinear superposition method.

[0217] In some specific embodiments, the matrix construction module 14 may specifically include:

[0218] A two-dimensional mesh establishment unit is used to establish a corresponding two-dimensional mesh based on the multi-node vibration sensing array.

[0219] A joint energy calculation unit is used to calculate the joint energy corresponding to each grid point in the two-dimensional grid.

[0220] A matrix construction unit is used to construct a corresponding energy distribution matrix based on the joint energy corresponding to all the grid points.

[0221] In some specific embodiments, the joint energy calculation unit may specifically include:

[0222] An azimuth calculation subunit is used to calculate the azimuth angle of each vibration sensor array at each grid point in the two-dimensional grid, and to determine the superimposed signal of each vibration sensor array obtained by beamforming analysis at the azimuth angle.

[0223] The Hilbert transform subunit is used to perform a Hilbert transform on the superimposed signal to obtain the envelope signal;

[0224] An absolute time-correction subunit is used to perform absolute time-correction on the envelope signal based on the distance between the grid points and the vibration sensing array, so as to obtain a time-corrected envelope signal.

[0225] The peak value determination subunit is used to determine the target time-corrected envelope signal corresponding to the target vibration sensing array with the largest energy, and to determine the peak value of the target time-corrected envelope signal;

[0226] The truncation interval determination sub-unit is used to determine the corresponding truncation interval based on the peak value, and extract the time-corrected envelope signal corresponding to each vibration sensing array within the truncation interval to calculate the joint energy corresponding to the grid point.

[0227] In some specific embodiments, the target location point determination module 15 may specifically include:

[0228] The clustering analysis unit is used to perform clustering analysis on the candidate location points based on the distance between the candidate location points and using a clustering algorithm to obtain the candidate location point set corresponding to each cluster;

[0229] The coordinate processing unit is used to average the coordinates of the candidate positioning points in the candidate positioning point set corresponding to each cluster to obtain the target candidate positioning point corresponding to each cluster.

[0230] The location point determination unit is used to determine whether the target candidate location point corresponding to each cluster is located within the maximum value range of the energy distribution matrix;

[0231] The target location point determination unit is used to determine the target candidate location point as the target location point if the target candidate location point is located within the maximum value range of the energy distribution matrix.

[0232] In some specific embodiments, the target location point determination module 15 may further include:

[0233] An angle correction value determination unit is used to determine the corresponding angle correction value from the pre-constructed angle correction field based on the candidate positioning point;

[0234] The orientation correction unit is used to correct the potential orientation of the moving signal source corresponding to each vibration sensor array using the angle correction value, so as to obtain the corrected potential orientation of the moving signal source.

[0235] The new candidate positioning point determination unit is used to determine a new target candidate positioning point based on the corrected potential direction of the moving signal source.

[0236] The iteration judgment unit is used to record the current iteration number in real time and determine whether the current iteration number meets the preset iteration number.

[0237] The first processing unit is configured to, when the current iteration number does not meet the preset iteration number, continue to execute the step of determining the corresponding angle correction value from the pre-constructed angle correction field and its subsequent steps based on the new target candidate positioning point.

[0238] The second processing unit is used to determine the new target candidate positioning point as the effective target candidate positioning point of the mobile signal source when the current iteration number meets the preset iteration number.

[0239] In some specific embodiments, the mobile source positioning device may further include:

[0240] The hammer impact signal processing module is used to process the original hammer impact signal received by each vibration sensor array in a preset hammer impact experiment based on the natural neighbor interpolation method to obtain the processed hammer impact signal.

[0241] The direction search module is used to perform beamforming analysis on the processed hammering signals received by each vibration sensor array to search for the direction of the target signal with the strongest energy corresponding to each vibration sensor array.

[0242] The direction comparison module is used to compare the direction of the target signal with the actual signal direction of the impact point to calculate the angle deviation corresponding to each vibration sensing array, and obtain the angle correction field corresponding to the multi-node vibration sensing array.

[0243] Figure 14 A schematic diagram of the structure of a terminal provided in an embodiment of this application. The terminal may include:

[0244] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0245] When the processor 502 executes the program, it implements the mobile source localization method provided in the above embodiments.

[0246] Furthermore, the terminal also includes:

[0247] Communication interface 503 is used for communication between memory 501 and processor 502.

[0248] The memory 501 is used to store computer programs that can run on the processor 502.

[0249] The memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0250] If the memory 501, processor 502, and communication interface 503 are implemented independently, they can be interconnected via a bus to communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one line is used in the diagram, but this does not imply that there is only one bus or only one type of bus.

[0251] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0252] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0253] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described mobile source location method.

[0254] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0255] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0256] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can read and execute instructions from and from an instruction execution system, apparatus or device).

[0257] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0258] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for locating a mobile source, characterized in that, The method includes: The raw signals received by each vibration sensor array in the multi-node vibration sensor array are acquired, and the signal to be processed is determined based on the raw signals; wherein, multiple vibration monitoring devices are deployed in a planar array distribution in each vibration sensor array; Based on the signal to be processed received by each vibration sensing array, a beamforming analysis is performed on each vibration sensing array to determine the potential direction of the moving signal source, and a candidate location point of the moving signal source is determined based on the potential direction of the moving signal source. Construct the energy distribution matrix corresponding to the multi-node vibration sensing array, and determine the target location point of the moving signal source from the candidate location points based on the energy distribution matrix; The step of determining candidate location points for the mobile signal source based on its potential direction includes: The potential directions of the mobile signal source are intersected pairwise to determine the corresponding intersection points; The energy confidence level of the intersection point is determined based on the waveform beam-gathering energy corresponding to the potential direction of the mobile signal source. The distance confidence level of the intersection point is determined based on the distance between the intersection point and each of the vibration sensor arrays. The target confidence level of the intersection point is determined based on the energy confidence level and the distance confidence level; Determine whether the target confidence level of the intersection point is greater than a preset confidence threshold; If the target confidence level at the intersection is greater than the preset confidence threshold, then the intersection is determined as a candidate location point for the mobile signal source. Determining the target location of the mobile signal source from the candidate location points based on the energy distribution matrix includes: Based on the distance between the candidate location points, and using a clustering algorithm to perform cluster analysis on the candidate location points, a set of candidate location points corresponding to each cluster is obtained; The coordinates of the candidate location points in the candidate location point set corresponding to each cluster are averaged to obtain the target candidate location point corresponding to each cluster. Determine whether the target candidate location point corresponding to each cluster is located within the maximum value range of the energy distribution matrix; If the target candidate location point is located within the range of the maximum value of the energy distribution matrix, then the target candidate location point is determined to be the target location point.

2. The mobile source positioning method according to claim 1, characterized in that, The step of determining the signal to be processed based on the original signal includes: The original signal is identified as the signal to be processed; Alternatively, the target signal can be extracted from the original signal to obtain the signal to be processed corresponding to each vibration sensing array.

3. The mobile source positioning method according to claim 2, characterized in that, The step of extracting the target signal from the original signal to obtain the signal to be processed corresponding to each vibration sensing array includes: The original signal is low-pass filtered to extract the target signal, thus obtaining the signal to be processed for each vibration sensing array. Alternatively, wavelet transform processing can be performed on the original signal to extract the target signal, thus obtaining the signal to be processed corresponding to each vibration sensing array.

4. The mobile source positioning method according to claim 1, characterized in that, After determining the signal to be processed based on the original signal, the process further includes: The signal to be processed is segmented according to a sliding time window with a preset time window length and a preset overlap rate to obtain a processed signal corresponding to each vibration sensing array, which contains multiple overlapping vibration signal segments.

5. The mobile source positioning method according to claim 1, characterized in that, The distance between each vibration monitoring device in each vibration sensing array is no greater than half the wavelength of the original signal.

6. The mobile source positioning method according to claim 1, characterized in that, The planar array can be any one of a planar square array, a planar circular array, or a planar hexagonal array.

7. The mobile source positioning method according to claim 1, characterized in that, The step of performing beamforming analysis on each vibration sensing array based on the signal to be processed received by each vibration sensing array to determine the potential direction of the moving signal source includes: For each signal incident direction, the arrival time of the signal to be processed corresponding to each vibration monitoring device is aligned based on the calculated theoretical arrival time difference of each vibration monitoring device in each vibration sensing array, and the time-aligned signals are superimposed to obtain the superimposed signal corresponding to each signal incident direction under each vibration sensing array. The waveform focusing energy corresponding to each signal incident direction under each vibration sensor array is determined based on the superimposed signal corresponding to each signal incident direction under each vibration sensor array. The potential direction of the moving signal source corresponding to each vibration sensor array is determined based on the waveform beamforming energy corresponding to the incident direction of each signal under each vibration sensor array.

8. The mobile source positioning method according to claim 7, characterized in that, The determination of the potential direction of the moving signal source corresponding to each vibration sensing array based on the waveform focusing energy corresponding to each signal incident direction under each vibration sensing array includes: Determine whether the waveform focusing energy corresponding to the incident direction of each signal under each vibration sensor array exceeds a preset energy threshold. When the waveform beam energy corresponding to the signal incident direction exceeds the preset energy threshold, the signal incident direction is determined to be the potential direction of the moving signal source, and the potential direction of the moving signal source corresponding to each vibration sensing array is obtained.

9. The mobile source positioning method according to claim 7, characterized in that, The superposition of the time-aligned signals includes: The time-aligned signals corresponding to each vibration monitoring device in each vibration sensing array are superimposed using either linear or nonlinear superposition methods.

10. The mobile source localization method according to claim 1, characterized in that, The construction of the energy distribution matrix corresponding to the multi-node vibration sensing array includes: A corresponding two-dimensional grid is established based on the multi-node vibration sensing array, and the joint energy corresponding to each grid point in the two-dimensional grid is calculated. A corresponding energy distribution matrix is ​​constructed based on the joint energy corresponding to all the grid points.

11. The mobile source localization method according to claim 10, characterized in that, The calculation of the joint energy corresponding to each grid point in the two-dimensional grid includes: For each grid point in the two-dimensional grid, calculate the azimuth angle of each vibration sensor array at the grid point, and determine the superimposed signal obtained by beamforming analysis of each vibration sensor array at the azimuth angle. Perform a Hilbert transform on the superimposed signal to obtain the envelope signal; Based on the distance between the grid points and the vibration sensing array, the envelope signal is absolutely time-corrected to obtain the time-corrected envelope signal. Determine the target time-corrected envelope signal corresponding to the target vibration sensing array with the largest energy, and determine the peak value of the target time-corrected envelope signal; Based on the peak value, a corresponding cutoff interval is determined, and the time-corrected envelope signal corresponding to each vibration sensing array within the cutoff interval is extracted to calculate the joint energy corresponding to the grid point.

12. The mobile source localization method according to claim 1, characterized in that, After averaging the coordinates of the candidate location points in the candidate location point set corresponding to each cluster to obtain the target candidate location points for each cluster, the method further includes: Based on the target candidate positioning point, the corresponding angle correction value is determined from the pre-constructed angle correction field, and the angle correction value is used to correct the potential direction of the moving signal source corresponding to each vibration sensor array to obtain the corrected potential direction of the moving signal source. New target candidate positioning points are determined based on the corrected potential direction of the moving signal source. Record the current iteration count in real time and determine whether the current iteration count meets the preset iteration count; If the current iteration count does not meet the preset iteration count, then based on the new target candidate positioning point, continue to execute the step of determining the corresponding angle correction value from the pre-constructed angle correction field and its subsequent steps; When the current iteration count meets the preset iteration count, the new target candidate location point is determined as the valid target candidate location point of the mobile signal source.

13. The mobile source localization method according to claim 12, characterized in that, Also includes: In the pre-set hammer impact experiment, the original hammer impact signal received by each vibration sensor array is processed based on the natural neighbor interpolation method to obtain the processed hammer impact signal. The processed hammer impact signal received by each vibration sensor array is subjected to beamforming analysis to search for the direction of the target signal with the strongest energy corresponding to each vibration sensor array. The target signal direction is compared with the actual signal direction at the impact point to calculate the angle deviation corresponding to each vibration sensing array, thereby obtaining the angle correction field corresponding to the multi-node vibration sensing array.

14. A mobile source positioning device, characterized in that, The device includes: The signal acquisition module is used to acquire the raw signals received by each vibration sensor array in the multi-node vibration sensor array, and determine the signal to be processed based on the raw signals; wherein, each vibration sensor array has multiple vibration monitoring devices deployed in a planar array distribution. A potential direction determination module is used to perform beamforming analysis on each vibration sensing array based on the signal to be processed received by each vibration sensing array, so as to determine the potential direction of the moving signal source. A candidate location point determination module is used to determine candidate location points of the mobile signal source based on the potential direction of the mobile signal source; A matrix construction module is used to construct the energy distribution matrix corresponding to the multi-node vibration sensing array. The target location point determination module is used to determine the target location point of the mobile signal source from the candidate location points based on the energy distribution matrix; The candidate location point determination module is specifically used for: The potential directions of the mobile signal source are intersected pairwise to determine the corresponding intersection points; The energy confidence level of the intersection point is determined based on the waveform beam-gathering energy corresponding to the potential direction of the mobile signal source. The distance confidence level of the intersection point is determined based on the distance between the intersection point and each of the vibration sensor arrays. The target confidence level of the intersection point is determined based on the energy confidence level and the distance confidence level; Determine whether the target confidence level of the intersection point is greater than a preset confidence threshold; If the target confidence level at the intersection is greater than the preset confidence threshold, then the intersection is determined as a candidate location point for the mobile signal source. The target location point determination module is specifically used for: Based on the distance between the candidate location points, and using a clustering algorithm to perform cluster analysis on the candidate location points, a set of candidate location points corresponding to each cluster is obtained; The coordinates of the candidate location points in the candidate location point set corresponding to each cluster are averaged to obtain the target candidate location point corresponding to each cluster. Determine whether the target candidate location point corresponding to each cluster is located within the maximum value range of the energy distribution matrix; If the target candidate location point is located within the range of the maximum value of the energy distribution matrix, then the target candidate location point is determined to be the target location point.

15. A terminal, characterized in that, include: The mobile source locator is a memory, a processor, and a mobile source locator program stored in the memory and executable on the processor, wherein the mobile source locator program, when executed by the processor, implements the steps of the mobile source locator method as described in any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that can be executed to implement the steps of the mobile source localization method as described in any one of claims 1 to 13.

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