CN118244365A - Buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis - Google Patents
Buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis Download PDFInfo
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Abstract
The application relates to a buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis, which comprises the following steps: collecting magnetic field data of a target area by using a magnetic field sensor, performing differential processing, and extracting to obtain a magnetic anomaly fingerprint signal; the target object is equivalent to a magnetic dipole, and the magnetic moment of the target object, the position of the target object and the shortest path CPA between the detection system and the target object are obtained by inversion of a particle swarm optimization algorithm based on a magnetic dipole model, magnetic anomaly fingerprint signals and track data of the magnetic field sensor; and calculating an induction section d based on the shortest path and the induction included angle theta, analyzing the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve, and judging whether the target object is a detection target by combining the constraint relation among any one or any two of the time-frequency domain characteristics, the magnetic moment size, the induction section d and the shortest path. The application realizes higher accuracy and reliability.
Description
Technical Field
The application relates to the technical field of electromagnetic detection, in particular to a buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis.
Background
Over 60 countries worldwide still have over 1.1 billions of non-explosive drugs buried underground, which not only threatens the life safety of people, but also severely pollutes large lands. Over the past 30 years, about 3 tens of thousands of people are severely injured each year due to a non-explosive event. Several studies have been established internationally with respect to this problem with the aim of increasing the level of skill in detecting and treating these dangerous objects. In view of this challenge, there is an urgent need to develop a high-efficiency detection technique, specifically for searching and identifying the non-explosive drugs buried in the ground, so as to realize near-ground dense scanning of the target area, obtain high-quality detection data, and be used for accurate positioning and identification of the target.
Existing detection of subsurface targets using magnetic anomaly detection has some limitations. First, existing methods focus on detecting the presence of an underground target, and the acquisition of key physical properties such as size, magnetic moment, depth, etc. of the target is limited. This is mainly because conventional magnetic field detection methods tend to ignore the effect of the target size on the magnetic anomaly signal when designed, resulting in an inability to accurately distinguish the size or shape of the target. Furthermore, while these methods may be positioned to the approximate location of the target, it is often difficult to provide enough information to accurately calculate the magnetic moment of the target or to analyze the depth of the target in depth. The magnetic moment is a measure for measuring the total magnetism of an object, and depth information relates to the safety and efficiency of subsequent excavation or treatment, which are extremely important parameters in evaluating potential dangerous objects such as non-explosive drugs.
The method is limited by the data analysis and processing capacity of the existing method, so that the description of the target buried object is not comprehensive enough, and the accuracy and efficiency of detection are affected. In practical applications, this may mean higher operational risks and lower task completion rates, especially in operational scenarios where high accuracy and safety are required.
Accordingly, to overcome these drawbacks, developing more advanced detection techniques that more fully identify and locate various physical characteristics of subsurface targets is an urgent need to address.
Disclosure of Invention
The embodiment of the application provides a buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis, which is used for detecting the existence of a non-explosive target object, deeply analyzing the attribute of the target object, distinguishing the target objects with different sizes and realizing higher accuracy and reliability.
In order to achieve the above object, an embodiment of the present application provides a method for detecting and identifying buried non-explosive based on magnetic anomaly fingerprint curve analysis, including:
a magnetic anomaly fingerprint acquisition step, namely acquiring magnetic field data of a target area by using a magnetic field sensor, performing differential processing, and extracting to obtain a magnetic anomaly fingerprint signal; the magnetic field sensor moves at a certain speed v from a preset path starting point to a preset path ending point based on the mounted detection system.
And obtaining target position information, namely, equivalent the target to be a magnetic dipole, inverting the magnetic moment of the target, the target position and the shortest path CPA (Closest Path Approach) between the detection system and the target by using a particle swarm optimization algorithm based on a magnetic dipole model, a magnetic anomaly fingerprint signal and track data of the magnetic field sensor, wherein the shortest path CPA is the buried depth of the target.
And a target object detection and identification step, namely calculating an induction section d based on the shortest path and the induction included angle theta, analyzing the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve, and judging whether the target object is a detection target by combining the constraint relation among any one or any two of the time-frequency domain characteristics, the magnetic moment size, the induction section d and the shortest path.
In some of these embodiments, the magnetically anomalous fingerprint acquisition step further comprises:
A data processing step, namely fitting and mapping the magnetic field data acquired by the magnetic field sensor with track data, integrating the data acquired by the magnetic field sensor with corresponding position data (such as GPS track) through fitting and mapping, so as to ensure that the magnetic field data can accurately correspond to an actual geographic position, further enable the subsequent data processing to be more accurate, and convert geographic coordinates (longitude and latitude) of the data into a Cartesian coordinate system taking meters as a unit, thereby improving the accuracy and efficiency of physical model calculation;
And a target data extraction step, namely extracting magnetic field data of the target area and corresponding track data from the converted data, wherein the track data is expressed as R (t) =R 0+v(t-t0),R0 and is used for indicating that the magnetic field sensor is at a nearest point of approach to the target object, namely, when R 0 is determined, the shortest path CPA can be obtained, v is used for indicating the moving speed of the magnetic field sensor, t is used for indicating the current time, and t 0 is used for indicating the moment that the magnetic field sensor is nearest to the target object.
In some embodiments, the target position information obtaining step further includes:
Bringing the trajectory data into a gradient tensor of the magnetic dipole model, and representing a magnetic induction intensity vector B as a three-component linear model;
And carrying out inversion by combining the three-component linear model and a vertical gradient model of the magnetic anomaly fingerprint signal, and estimating the positions x, y, z, magnetic moments m and R 0 of the target object based on a particle swarm optimization algorithm to obtain the optimal magnetic moment, the position of the target object, the shortest path CPA between the detection system and the target object, the shortest path CPA and an induced included angle.
In some of these embodiments, the three-component linear model is represented as a computational model as follows:
wherein mu 0 is magnetic permeability, X, y, z are coordinates of the target position, m x、my、mz is the magnetic moment vector of the target, and the characteristic amount of time τ= (t-t 0)v/R0＝(x-x0)/R0,f1、f2、f3 is an anderson function, expressed as ：f1(τ)＝1/(1+τ2)52,,f2(τ)＝τ/(1+τ2)52,f3(τ)＝τ2/(1+τ2)52.
In some of these embodiments, the vertical gradient model is represented as a computational model as follows:
Wherein,
Phi is the local geomagnetic inclination angle, beta is the local geomagnetic declination angle, and f 4、f5、f6 is the anderson function.
In some embodiments, the time domain features in the time-frequency domain features include signal amplitude, signal duration, and bandwidth, and the frequency domain features in the time-frequency domain features include frequency bandwidth.
In some of these embodiments, the constraint relationship comprises: and determining the constraint relation between the time domain features and the magnetic moment of the target object and the constraint relation between the frequency domain features and the speed and the constraint relation between the frequency domain features and the shortest path CPA.
In some of these embodiments, the signal amplitude is proportional to the magnetic moment magnitude, the signal amplitude is inversely proportional to the third power of the shortest path CPA, and the signal duration is proportional to the shortest path CPA.
In some of these embodiments, the frequency bandwidth is proportional to the speed and the frequency bandwidth is inversely proportional to the shortest path CPA.
In some embodiments, based on different types of non-explosive bombs and different magnetic anomaly fingerprint signals under different test environments, the magnetic moment size, the target position, the shortest path CPA, the induction interval and the time-frequency domain characteristics obtained by analysis of batch reaction are obtained, and a database is built by any combination of the relative relations of the magnetic moment size, the target position, the shortest path CPA, the induction interval and the time-frequency domain characteristics, so that a knowledge graph is formed in a correlation mode or a prediction model is formed in a training mode, and automatic positioning of the target is realized and the size and the type of the target are predicted.
Compared with the related art, the buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis provided by the embodiment of the application realizes accurate positioning of the non-explosive and identification of the target by combining analysis of curve characteristics, magnetic moment size, shortest path CPA and the like of magnetic anomaly fingerprint signals through the magnetic dipole model and the vertical gradient quantity inversion of the magnetic moment and the shortest path CPA of the target, and the inversion magnetic moment error of the embodiment of the application is less than 3%, and the shortest path CPA (i.e. depth) error can reach 2cm, so that higher accuracy and reliability are provided for detection and identification of the non-explosive.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic diagram of the principle of operation of buried small object detection and identification in accordance with the present application;
FIG. 2 is a flow chart of a buried unperforated bomb detection identification method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a buried unperforated bomb detection and identification method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a probing principle according to an embodiment of the present application;
FIG. 5 is a schematic diagram of data processing principle of a buried non-explosive detection and identification method according to an embodiment of the present application;
FIG. 6 is a schematic representation of time domain characteristics of two non-detonating cartridges according to an embodiment of the present application;
FIG. 7 is a diagram showing the relationship between the time domain feature and the magnetic moment, CPA, FIG. 7 (a) is a diagram showing the relationship between the time domain feature and the magnitude of the magnetic moment, and FIG. 7 (b) is a diagram showing the relationship between the time domain feature and the CPA, according to an embodiment of the present application;
Fig. 8 is a schematic diagram of the relationship among the test signal curve, the sensing section and the CPA according to an embodiment of the present application, fig. 8 (a) is a schematic diagram of the relationship among the signal curve, the sensing section and the CPA of the duplex-warship shell, and fig. 8 (b) is a schematic diagram of the relationship among the signal curve, the sensing section and the CPA of the 120 mm caliber shell;
FIG. 9 is a graph showing the effect of fitting the inversion signal to the original signal in accordance with a preferred embodiment of the present application;
FIG. 10 is a graph of the effect of fitting an inversion gradient to an original gradient in accordance with a preferred embodiment of the present application;
FIG. 11 is a plot of inversion position effects of a preferred embodiment of the application.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
In order to solve the problems of the existing buried nonexplosive bomb, the application provides a buried small target detection and identification method based on magnetic anomaly fingerprint curve analysis. As shown in fig. 1, the basic principle is to effectively detect and rapidly identify buried small objects by detecting and analyzing weak magnetic field anomalies generated by underground or underwater objects with high accuracy and high resolution. The core technology is to utilize the abnormal signal generated by the magnetic difference between the underground target and the surrounding environment, and combine advanced signal processing and parameter inversion algorithm to realize the accurate positioning and identification of the target. This not only improves detection efficiency and speed, but also enables remote detection without touching the target, greatly reducing the risk of field operators.
The embodiment provides a buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis. Fig. 2 is a flowchart of a method for detecting and identifying buried non-explosive charges based on magnetic anomaly fingerprint curve analysis according to an embodiment of the present application, and fig. 3 is a schematic diagram of a method for detecting and identifying buried non-explosive charges according to an embodiment of the present application, as shown in fig. 2-3, the flowchart includes the following steps:
A magnetic anomaly fingerprint acquisition step S1, namely acquiring magnetic field data of a target area by using a magnetic field sensor, performing differential processing, and extracting to obtain a magnetic anomaly fingerprint signal, which is also called a magnetic anomaly fingerprint feature; in order to realize the step S1 of acquiring the magnetic anomaly fingerprint, a magnetic field sensor constructed based on magnetometers is arranged in advance in a target area, and the differential processing is to strengthen magnetic signals of underground targets and inhibit background noise by comparing at least two collected magnetic field data, wherein the magnetic field sensor moves at a certain speed v from a preset path starting point to a preset path ending point based on a carried detection system, as shown in fig. 4. Wherein, the step S1 of acquiring a magnetic anomaly fingerprint further includes:
A data processing step S101, wherein the magnetic field data acquired by the magnetic field sensor is mapped with the track data in a fitting way, and the data acquired by the magnetic field sensor and the corresponding position data (such as a GPS track) are integrated through the fitting way, so that the magnetic field data can be accurately corresponding to the actual geographic position, the subsequent data processing is more accurate, the geographic coordinates (longitude and latitude) of the data are converted into a cartesian coordinate system with meter as a unit, the accuracy and efficiency of the calculation of the physical model are improved, and the data acquisition, the magnetic map fitting, the coordinate conversion and the data mapping processes shown in fig. 5 are combined;
And a target data extraction step S102, wherein the magnetic field data of the target area and the corresponding track data are extracted from the converted data, the track data are expressed as R (t) =r 0+v(t-t0),R0 and are used for indicating that the magnetic field sensor is at a point of approach closest to the target object, as shown in fig. 4, when R 0 is determined, the shortest path CPA can be obtained, v is used for indicating the moving speed of the magnetic field sensor, t is used for indicating the current time, and t 0 is used for indicating the moment that the magnetic field sensor is closest to the target object.
Based on the target region magnetic field data and the track data extracted in the step S102, a target object position information obtaining step S2 is executed to perform parameter inversion, specifically:
The target position information obtaining step S2 is to equivalent a target to a magnetic dipole, and inversion is carried out by utilizing a particle swarm optimization algorithm based on a magnetic dipole model, a magnetic anomaly fingerprint signal and track data of the magnetic field sensor to obtain the magnetic moment of the target, the position of the target and the shortest path CPA of the detection system and the target, wherein the shortest path CPA is the buried depth of the target as shown in reference to FIG. 4;
Wherein, the target object position information obtaining step further includes:
Bringing the trajectory data into a gradient tensor of the magnetic dipole model, and representing a magnetic induction intensity vector B as a three-component linear model; the magnetic induction vector B is expressed as:
r，m＞＝mxrx+myry+mzrz
Wherein mu 0 is magnetic permeability, x, y and z are coordinates of a target object position, r is a displacement vector between a detection point and the target object, m is a magnetic moment of the target object, m x、my、mz is a magnetic moment vector of the target object, and a gradient of three components simplified by a Cronecker function delta ij is as follows:
Based on the above equation, assuming that the detection system moves linearly parallel to the x-axis at a constant speed, the above trajectory expression R (t) =r 0+v(t-t0 is obtained, R (t) is substituted into the above expression of the magnetic induction vector B, and the magnetic induction vector B is written as a linear combination of "Anderson functions", to obtain a three-component linear model, expressed as the following calculation model:
Characteristic amount of time τ= (t-t 0)v/R0＝(x-x0)/R0,f1、f2、f3 is an anderson function, represented as ：f1(τ)＝1/(1+τ2)52,,f2(τ)＝τ/(1+τ2)52,f3(τ)＝τ2/(1+τ2)52,x0 magnetic field sensor at the target x-axis coordinate closest to the target.
For a total field sensor, since the geomagnetic field magnitude is much larger than the magnetic anomaly signal magnitude, the observed signal can be approximated as follows:
Wherein/>
Phi is the local geomagnetic inclination angle, and beta is the local geomagnetic declination angle. Based on this, the total field magnetic anomaly fingerprint signal expression can be obtained as:
where the mu x、μy and mu z components represent projections of the magnetic field strength in three different directions, generally aligned with the coordinate axes of the sensor.
The vertical gradient of the resulting magnetic anomaly fingerprint signal is then represented by:
The introduction of the anderson function f 4、f5、f6 to the vertical gradient of the magnetic anomaly fingerprint signal is simplified as:
Wherein:
And carrying out inversion by combining the three-component linear model and a vertical gradient model of the magnetic anomaly fingerprint signal, estimating the positions x, y, z, magnetic moment m and R 0 of the target object based on a particle swarm optimization algorithm to obtain the optimal magnetic moment, the position of the target object and the shortest path CPA of the detection system and the target object, wherein the shortest path CPA is preconfigured with an induction included angle range, such as 90-120 degrees, and the shortest path CPA corresponds to an induction included angle theta in the inversion process.
The method comprises the steps of firstly taking the mean value of magnetic field data of a target area as T inverse solution to obtain values of x 0、R0、p1、p2 and p 3, and then taking the ratio of the difference value of magnetometers (namely the magnetic field intensity difference value at two different heights) to the base line distance as a vertical gradient G z to obtain y and z. And then, calculating the magnetic moment according to p 1、p2 and p 3, x, y and z, and carrying out iteration by using a particle swarm optimization algorithm in the process to obtain the optimal magnetic moment, the position of the target and the shortest path CPA between the detection system and the target.
And S3, detecting and identifying the target object, namely calculating an induction section d based on the shortest path CPA and the induction included angle theta, analyzing the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve, and judging whether the target object is a detection target by combining the time-frequency domain characteristics, the magnetic moment, the induction section d and the constraint relation between any one or any two of the shortest paths. The time domain features in the time-frequency domain features comprise signal amplitude, signal duration and wave width, the time domain waveform of the magnetic anomaly fingerprint signal curve has a single peak form and a double peak form, the frequency domain features in the time-frequency domain features comprise frequency band width, and the magnetic anomaly fingerprint signals are concentrated in a low-frequency band.
Taking the signal amplitude and the wave width in the time domain feature as an example, fig. 6 is a time domain signal of two non-detonated shells under the same test condition, referring to fig. 6, the test speed is calculated according to the start point and the end point of the test path, the horizontal axis in the figure is the length of the detection track, the vertical axis in the figure is the signal strength, 60 is the 60 mortar in the figure, and it can be seen that the signal amplitude and the wave width of the 60 mortar and the duplex warship shell in the figure have significant difference under the same test condition (the shortest path CPA and the speed are consistent), so that whether the target object is detected or not can be judged based on the signal amplitude and the wave width.
In addition, the induction intervals of the magnetic abnormal fingerprint signal curves of different types of non-explosive bombs and the shortest path CPA also have obvious differences, and can be used for identifying the target object. As shown in fig. 8, in the embodiment of the present application, a duplex warship shell (length 53 cm) and a 120 mm caliber shell (length 240 cm) are taken as examples for testing, and the relative relationship between the magnetic anomaly signal and the shortest path obtained by inversion in the detection process is displayed in the same way, wherein the horizontal axes in fig. 8 (a) - (b) are the length of the detection track, the vertical axis in the lower half of the figure is the CPA depth, and the vertical axis in the upper half of the figure is the signal strength. As shown in fig. 8 (a), the limit test CPA obtained in the step S2 of obtaining the target object position information of the present application is 150cm, the sensing interval d is about 5.1m, and when the sensing included angle θe [90,120], the effective range of the continuous path of the target magnetic anomaly signal is smaller than 2 v 3CPA. As shown in fig. 8 (b), the limit test cpa=550 cm of the 120 mm caliber bomb, the sensing interval d is about 15m, and the sensing interval d, the shortest path CPA and the sensing included angle θ are constrained by a trigonometric function.
In some of these embodiments, the constraint relationship comprises: and determining the constraint relation between the time domain features and the magnetic moment of the target object and the constraint relation between the frequency domain features and the speed and the constraint relation between the frequency domain features and the shortest path CPA. Specific:
the signal amplitude is in direct proportion to the magnetic moment;
the signal amplitude is inversely proportional to the third power of the shortest path CPA;
The signal duration is proportional to the shortest path CPA.
In addition, the constraint relation is obtained by reasoning according to the time bandwidth product theorem, and therefore, the constraint relation also comprises: the frequency bandwidth is proportional to the speed and inversely proportional to the shortest path CPA.
The above constraint relationship is exemplified below.
Taking the proportional relationship between the signal amplitude and the magnetic moment as an example, referring to fig. 7 (a), the horizontal axis in the figure represents time Times, the vertical axis represents magnetic field strength, and when the magnetic moment is 1.0A.m 2、1.5A.m2、2.0A.m2、2.5A.m2, the amplitude of the magnetic anomaly signal increases as the magnetic moment increases.
Taking the proportional relationship between the signal amplitude and the third power of the shortest distance CPA as an example, referring to fig. 7 (b), the horizontal axis in the figure is time, the vertical axis is magnetic field strength, and when the magnetic field strength is 1.5m, 2.0m, 2.5m, and 3.0m, the signal amplitude increases as the CPA increases and the magnetic field strength is less than zero, the signal amplitude increases as the CPA decreases.
Therefore, through the steps, the embodiment of the application can more accurately read the information hidden by the time-frequency domain characteristics of the magnetic anomaly fingerprint signals by combining the analysis of the time-frequency domain characteristics, the magnetic moment, the induction interval d and the shortest path, so as to distinguish different buried unperforated bullets, improve the target recognition accuracy and provide a new unperforated bullet detection and recognition means.
The embodiments of the present application will be described and illustrated below by means of preferred embodiments.
In the preferred embodiment, inversion is performed by taking sea 37-55 shells as an example, the actual burying depth is 20 cm, the actual magnetic moment of the sea 37-55 shells is 0.185A.m2, wherein the horizontal axis of FIG. 9 is time Times, the vertical axis is magnetic field strength, the degree of fitting of inversion signals obtained in the inversion process and magnetic anomaly fingerprint curves is higher, the horizontal axis of FIG. 10 is time Times, the vertical axis is vertical gradient G, and the degree of fitting of gradient data obtained in the inversion process and original gradients of the signals is higher. The magnitude of the inversion magnetic moment obtained through inversion is 0.1902A.m2, and compared with the actual magnetic moment, the error is only 2.8%; the inversion depth was 18cm with a minimum error of 2cm from the actual buried depth. Based on the method, the embodiment of the application can more accurately invert the magnetic moment and depth information of the buried small target by introducing the vertical gradient and the particle swarm optimization algorithm, and provides higher accuracy and reliability for detecting and identifying the non-explosive bomb.
Correspondingly, as shown in fig. 11, the horizontal axis of fig. 11 is an X-axis coordinate, the vertical axis is a Y-axis coordinate, and the inversion position obtained in the inversion process is shown to be close to the real position of the buried object, so that the buried object can be accurately positioned.
In another embodiment, in order to facilitate the prediction and analysis of the size and the type of the target object, the embodiment of the application can obtain the magnetic moment size, the target object position, the shortest path CPA, the sensing interval and the time-frequency domain characteristics obtained by analysis of the target object in batch reaction based on the differences of the types of the nonexplosive objects and the magnetic anomaly fingerprint signals under different test environments, and can randomly combine the relative relations of the magnetic moment size, the target object position, the shortest path CPA, the sensing interval and the time-frequency domain characteristics to establish a database, so that a knowledge graph is formed in a correlated manner or a prediction model is formed in a trained manner, and the automatic positioning of the target object and the prediction of the size and the type of the target object are realized.
In the embodiment of the application, magnetic anomaly fingerprint signals of different types of non-explosive bombs are collected in advance, the signals are obtained by carrying out field measurement on different environments and different types of targets, a knowledge graph is constructed on the basis of a database based on the magnetic moment size of the targets, the positions of the targets, the shortest path CPA, an induction interval and time-frequency domain characteristics obtained by analysis of batch reaction, and the parameters and relative relations thereof are compiled into the graph, wherein the relative relations are configured on the basis of constraint conditions of the application; the machine learning model is trained by utilizing the data in the database, such as decision trees, random forests or neural networks, so that the model can predict the types and sizes of the targets according to the magnetic moment sizes, positions, CPAs, sensing intervals and time-frequency domain characteristics of the targets, thereby realizing the automatic positioning of the targets and the prediction of the detailed characteristics by effectively utilizing the magnetic force data, and having important application values in many fields such as geological exploration, archaeology, safety inspection and the like.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. The buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis is characterized by comprising the following steps of:
A magnetic anomaly fingerprint acquisition step, namely acquiring magnetic field data of a target area by using a magnetic field sensor, performing differential processing, and extracting to obtain a magnetic anomaly fingerprint signal;
the method comprises the steps of obtaining target position information, namely, equivalent target is magnetic dipole, and magnetic moment of the target, target position and shortest path CPA of a detection system and the target are obtained by inversion of a particle swarm optimization algorithm based on a magnetic dipole model, magnetic anomaly fingerprint signals and track data of the magnetic field sensor;
And a target object detection and identification step, namely calculating an induction section d based on the shortest path and the induction included angle theta, analyzing the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve, and judging whether the target object is a detection target by combining the constraint relation among any one or any two of the time-frequency domain characteristics, the magnetic moment size, the induction section d and the shortest path.
2. The method for detecting and identifying buried non-explosive based on magnetic anomaly fingerprint curve analysis according to claim 1, wherein the step of acquiring the magnetic anomaly fingerprint further comprises:
A data processing step, namely fitting and mapping magnetic field data acquired by the magnetic field sensor with track data, and converting geographic coordinates of the data into a Cartesian coordinate system;
A target data extraction step of extracting magnetic field data of a target region and corresponding trajectory data from the converted data, wherein the trajectory data is expressed as: r (t) =r 0+v(t-t0),
R 0 is used to indicate that the magnetic field sensor is at a closest approach point to the target object, v is used to indicate the moving speed of the magnetic field sensor, t is used to indicate the current time, and t 0 is used to indicate the moment that the magnetic field sensor is closest to the target object.
3. The method for detecting and identifying buried non-explosive charges based on magnetic anomaly fingerprint curve analysis according to claim 2, wherein the target position information obtaining step further comprises:
Bringing the trajectory data into a gradient tensor of the magnetic dipole model, and representing a magnetic induction intensity vector B as a three-component linear model;
And carrying out inversion by combining the three-component linear model and a vertical gradient model of the magnetic anomaly fingerprint signal, and estimating the positions x, y, z, magnetic moments m and R 0 of the target object based on a particle swarm optimization algorithm to obtain the optimal magnetic moment, the position of the target object and the shortest path CPA between the detection system and the target object.
4. The buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 3, wherein the three-component linear model is represented as a calculation model as follows:
wherein mu 0 is magnetic permeability, X, y, z are coordinates of the target position, m x、my、mz is the magnetic moment vector of the target, and the characteristic amount of time τ= (t-t 0)v/R0＝(x-x0)/R0,f1、f2、f3 is an anderson function, expressed as ：f1(τ)＝1/(1+τ2)5/2,,f2(τ)＝τ/(1+τ2)5/2,f3(τ)＝τ2/(1+τ2)5/2.
5. The buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 4, wherein the vertical gradient model is represented as a calculation model as follows:
Wherein,
Phi is the local geomagnetic inclination angle, and beta is the local geomagnetic declination angle.
6. The buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 2, wherein the time domain features in the time-frequency domain features comprise signal amplitude, signal duration and wave width, and the frequency domain features in the time-frequency domain features comprise frequency bandwidth.
7. The buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 6, wherein the constraint relation comprises: and determining the constraint relation between the time domain features and the magnetic moment of the target object and the constraint relation between the frequency domain features and the speed and the constraint relation between the frequency domain features and the shortest path CPA.
8. The buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 7, wherein the signal amplitude is in a direct relation with the magnetic moment, the signal amplitude is in an inverse relation with the third power of the shortest path CPA, and the signal duration is in a direct relation with the shortest path CPA.
9. The buried non-explosive detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 7, wherein the frequency bandwidth is in a direct proportion relation with the speed, and the frequency bandwidth is in an inverse proportion relation with the shortest path CPA.
10. The buried non-explosive detection and identification method based on analysis of magnetic anomaly fingerprint curves according to any one of claims 1 to 9, wherein a database is established based on the relative relation of the magnetic moment of a target, the position of the target, the shortest path CPA, the induction interval and the time-frequency domain characteristics obtained by analysis of mass inversion based on different types of non-explosive and different magnetic anomaly fingerprint signals under different test environments.
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