CN118244365A - A method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis - Google Patents

Abstract

The present application relates to a method for detecting and identifying buried unexploded bombs based on the analysis of magnetic anomaly fingerprint curves, wherein the method includes: using a magnetic field sensor to collect magnetic field data of a target area for differential processing, and extracting a magnetic anomaly fingerprint signal; treating the target object as a magnetic dipole, and using a particle swarm optimization algorithm to invert the magnetic moment size, target position, and the shortest path CPA between the detection system and the target object based on the magnetic dipole model, the magnetic anomaly fingerprint signal, and the trajectory data of the magnetic field sensor; calculating the induction interval d based on the shortest path and the induction angle θ, and analyzing the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve, and combining the time-frequency domain characteristics, the magnetic moment size, the induction interval d, and the constraint relationship between any one or any two of the shortest path to determine whether the target object is a detection target. Higher accuracy and reliability are achieved through the present application.

Description

A method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis

Technical Field

The present application relates to the field of electromagnetic detection technology, and in particular to a method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis.

Background technique

There are still more than 110 million pieces of unexploded ordnance buried underground in more than 60 countries around the world, which not only threatens the lives and safety of the people, but also seriously pollutes large areas of land. In the past 30 years, about 30,000 people have been seriously injured each year due to unexploded ordnance accidents. Several research projects have been set up internationally to improve the technical level of detection and handling of these dangerous items. In the face of this challenge, there is an urgent need to develop an efficient detection technology that specifically searches for and identifies unexploded ordnance buried underground, so as to achieve intensive near-ground scanning of the target area and obtain high-quality detection data for accurate positioning and identification of the target.

There are some limitations in the existing use of magnetic anomaly detection in underground target detection. First, the existing methods focus on detecting the existence of underground targets, while the acquisition of specific attributes of the target, such as size, magnetic moment, depth and other key physical characteristics is relatively limited. This is mainly because the conventional magnetic field detection methods often ignore the impact of target size on magnetic anomaly signals when designing, resulting in the inability to accurately distinguish the size or shape of the target. In addition, although these methods can locate the approximate position of the target, it is usually difficult to provide enough information to accurately calculate the magnetic moment of the target or to deeply analyze the depth of the target. The magnetic moment is a measure of the overall magnetism of an object, and the depth information is related to the safety and efficiency of subsequent excavation or processing. These are extremely important parameters when evaluating potential hazardous items such as unexploded ordnance.

Limited by the data analysis and processing capabilities of existing methods, the description of the target buried objects is not comprehensive, affecting the accuracy and efficiency of detection. In practical applications, this may mean higher operational risks and lower task completion rates, especially in operational scenarios that require high precision and high safety.

Therefore, in order to overcome these shortcomings, it is an urgent issue to develop more advanced detection technologies that can more comprehensively identify and locate the various physical characteristics of underground targets.

Summary of the invention

The embodiment of the present application provides a buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis, which can detect the existence of unexploded bomb targets, deeply analyze the properties of the targets, distinguish targets of different sizes, and achieve higher accuracy and reliability.

To achieve the above-mentioned purpose, the embodiment of the present application provides a method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis, comprising:

The magnetic anomaly fingerprint acquisition step uses a magnetic field sensor to collect magnetic field data of the target area for differential processing to extract a magnetic anomaly fingerprint signal; wherein the magnetic field sensor moves at a certain speed v from the starting point of a preset path to the end point of a preset path based on the onboard detection system.

In the step of acquiring the target object position information, the target object is equivalent to a magnetic dipole, and the magnetic moment size of the target object, the target object position and the shortest path CPA (Closest Path Approach) between the detection system and the target object are obtained by inverting the magnetic dipole model, the magnetic anomaly fingerprint signal and the trajectory data of the magnetic field sensor using a particle swarm optimization algorithm. The shortest path CPA is the burial depth of the target object.

The target object detection and identification step calculates the sensing interval d based on the shortest path and the sensing angle θ, and analyzes the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve, and combines the time-frequency domain characteristics, the magnetic moment size, the sensing interval d and the shortest path or the constraint relationship between any one or two to determine whether the target object is a detection target.

In some embodiments, the magnetic anomaly fingerprint acquisition step further comprises:

The data processing step is to fit and map the magnetic field data collected by the magnetic field sensor with the trajectory data, and integrate the data collected by the magnetic field sensor and the corresponding position data (such as GPS trajectory) through fitting mapping to ensure that the magnetic field data can accurately correspond to the actual geographical location, so that subsequent data processing is more accurate, and the geographical coordinates (latitude and longitude) of the data are converted into a Cartesian coordinate system with meters as the unit, so as to improve the accuracy and efficiency of the physical model calculation;

The target data extraction step extracts the magnetic field data of the target area and the corresponding trajectory data from the converted data, wherein the trajectory data is expressed as R(t)= _R0 +v( _tt0 ), _R0 is used to indicate that the magnetic field sensor is at the closest approach point to the target object, that is, when _R0 is determined, the shortest path CPA can be obtained, v is used to indicate the moving speed of the magnetic field sensor, t is used to indicate the current time, and _t0 is used to indicate the moment when the magnetic field sensor is closest to the target object.

In some embodiments, the target object location information acquisition step further includes:

Bring the trajectory data into the gradient tensor of the magnetic dipole model, and express the magnetic induction intensity vector B as a three-component linear model;

The three-component linear model and the vertical gradient model of the magnetic anomaly fingerprint signal are inverted, and the target position x, y, z, magnetic moment m, and _R0 are estimated based on the particle swarm optimization algorithm to obtain the optimal magnetic moment size, target position, and the shortest path CPA between the detection system and the target, the shortest path CPA, and the induction angle.

In some embodiments, the three-component linear model is represented by the following computational model:

Where _μ0 is the magnetic permeability, x, y, z are the coordinates of the target object's position, m _x , my _y , m _z are the magnetic moment vectors of the target object, the characteristic time quantity τ = (tt ₀ )v/R ₀ = (xx ₀ )/R ₀ , f ₁ , f ₂ , f ₃ are Anderson functions, expressed as: f ₁ (τ) = 1/(1+τ ² ) ⁵² , f ₂ (τ) = τ/(1+τ ² ) ⁵² , f ₃ (τ) = τ ² /(1+τ ² ) ⁵² .

In some embodiments, the vertical gradient model is represented by the following calculation model:

in,

φ is the local geomagnetic inclination, β is the local geomagnetic declination, and f ₄ , f ₅ , and f ₆ are Anderson functions.

In some embodiments, the time domain features in the time-frequency domain features include signal amplitude, signal duration, and wave width, and the frequency domain features in the time-frequency domain features include frequency bandwidth.

In some of the embodiments, the constraint relationship includes: determining the constraint relationship between the time domain characteristics and the magnetic moment size of the target object and the shortest path CPA, and determining the constraint relationship between the frequency domain characteristics and the speed and the shortest path CPA.

In some of the embodiments, the signal amplitude is proportional to the magnitude of the magnetic moment, the signal amplitude is inversely proportional to the cube of the shortest path CPA, and the signal duration is proportional to the shortest path CPA.

In some embodiments, the bandwidth is directly proportional to the speed, and the bandwidth is inversely proportional to the shortest path CPA.

In some of the embodiments, based on the different types of unexploded bombs and the differences in their magnetic anomaly fingerprint signals under different test environments, the target magnetic moment size, target position, shortest path CPA, sensing interval and time-frequency domain characteristics obtained by batch inversion are obtained, and a database is established by arbitrarily combining the relative relationship between the target magnetic moment size, target position, shortest path CPA, sensing interval and the time-frequency domain characteristics, so as to associate and form a knowledge graph or train to form a prediction model, so as to realize automatic positioning of the target and predict the size and type of the target.

Compared with the related art, the buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis provided in the embodiment of the present application inverts the magnetic moment and shortest path CPA of the target object through the magnetic dipole model and the vertical gradient quantity, and accurately locates the unexploded bomb and identifies the target by combining the curve characteristics, magnetic moment size, shortest path CPA and other analyses of the magnetic anomaly fingerprint signal. After testing, the inverted magnetic moment error of the embodiment of the present application is less than 3%, and the shortest path CPA (i.e., depth) error can reach 2 cm, which provides higher accuracy and reliability for the detection and identification of unexploded bombs.

Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

FIG1 is a schematic diagram of the working principle of buried small target detection and identification according to the present application;

FIG2 is a flow chart of a method for detecting and identifying buried unexploded bombs according to an embodiment of the present application;

FIG3 is a schematic diagram of the principle of a method for detecting and identifying buried unexploded bombs according to an embodiment of the present application;

FIG4 is a schematic diagram of a detection principle according to an embodiment of the present application;

FIG5 is a schematic diagram of the data processing principle of the buried unexploded bomb detection and identification method according to an embodiment of the present application;

FIG6 is a schematic diagram of time domain characteristics of two unexploded bombs according to an embodiment of the present application;

FIG. 7 is a diagram showing the relationship between the time domain characteristics and the magnetic moment and CPA according to an embodiment of the present application. FIG. 7( a ) is a schematic diagram showing the relationship between the time domain characteristics and the magnitude of the magnetic moment, and FIG. 7( b ) is a schematic diagram showing the relationship between the time domain characteristics and the CPA.

FIG8 is a schematic diagram of the relationship among the test signal curve, the sensing interval and the CPA according to an embodiment of the present application. FIG8(a) is a schematic diagram of the relationship among the signal curve, the sensing interval and the CPA of a twin-linked naval shell. FIG8(b) is a schematic diagram of the relationship among the signal curve, the sensing interval and the CPA of a 120 mm caliber bomb.

FIG9 is a diagram showing the fitting effect of the inversion signal and the original signal according to a preferred embodiment of the present application;

FIG10 is a diagram showing the fitting effect of the inversion gradient and the original gradient in a preferred embodiment of the present application;

FIG. 11 is a diagram showing the inversion position effect of a preferred embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And/or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and/or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

In order to solve the problems existing in the existing buried unexploded bombs, this application provides a method for detecting and identifying buried small targets based on the analysis of magnetic anomaly fingerprint curves. As shown in Figure 1, the basic principle is to effectively detect and quickly identify buried small targets by detecting and analyzing the weak magnetic field anomalies generated by underground or underwater targets with high precision and high resolution. The core technology is to use the abnormal signals generated by the magnetic difference between the underground target and the surrounding environment, combined with advanced signal processing and parameter inversion algorithms, to achieve accurate positioning and identification of the target. This not only improves the detection efficiency and speed, but also enables long-distance detection without contacting the target, greatly reducing the risk of on-site operators.

This embodiment provides a method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis. FIG2 is a flow chart of the method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis according to an embodiment of the present application, and FIG3 is a schematic diagram of the principle of the method for detecting and identifying buried unexploded bombs according to an embodiment of the present application. As shown in FIG2-3, the process includes the following steps:

The magnetic anomaly fingerprint acquisition step S1 uses a magnetic field sensor to collect magnetic field data of the target area for differential processing, and extracts a magnetic anomaly fingerprint signal, also known as a magnetic anomaly fingerprint feature; in order to implement the magnetic anomaly fingerprint acquisition step S1, the present application pre-arranges a magnetic field sensor based on a magnetometer in the target area, and the differential processing is to strengthen the magnetic signal of the underground target object and suppress background noise by comparing at least two collected magnetic field data, wherein the magnetic field sensor moves at a certain speed v from the starting point of the preset path to the end point of the preset path based on the detection system carried, as shown in Figure 4. The magnetic anomaly fingerprint acquisition step S1 further includes:

Data processing step S101, fitting and mapping the magnetic field data collected by the magnetic field sensor with the trajectory data, integrating the data collected by the magnetic field sensor and the corresponding position data (such as GPS trajectory) through fitting and mapping, to ensure that the magnetic field data can accurately correspond to the actual geographical location, so that the subsequent data processing is more accurate, and converting the geographical coordinates (latitude and longitude) of the data into a Cartesian coordinate system with meters as the unit, improving the accuracy and efficiency of the physical model calculation, combining the data collection, magnetic map fitting, coordinate conversion, and data mapping process shown in Figure 5;

In the target data extraction step S102, the magnetic field data of the target area and the corresponding trajectory data are extracted from the converted data, wherein the trajectory data is expressed as R(t)=R ₀ +v(tt ₀ ), R ₀ is used to indicate that the magnetic field sensor is at the closest approach point to the target object. As shown in FIG4 , when R ₀ is determined, the shortest path CPA can be obtained, 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 when the magnetic field sensor is closest to the target object.

Based on the target area magnetic field data and trajectory data extracted in step S102, the target object position information acquisition step S2 is executed to perform parameter inversion, specifically:

In the target position information acquisition step S2, the target is equivalent to a magnetic dipole, and the magnetic moment size of the target, the target position and the shortest path CPA between the detection system and the target are obtained by inverting the magnetic dipole model, the magnetic anomaly fingerprint signal and the trajectory data of the magnetic field sensor using a particle swarm optimization algorithm. As shown in FIG4 , the shortest path CPA is the burial depth of the target;

Wherein, the target object location information acquisition step further includes:

The trajectory data is brought into the gradient tensor of the magnetic dipole model, and the magnetic induction intensity vector B is expressed as a three-component linear model; the magnetic induction intensity vector B is expressed as:

r，m＞＝m _x r _x +my _{r y +} m _z r _z

Where _μ0 is the magnetic permeability, x, y, z are the coordinates of the target position, r is the displacement vector between the detection point and the target, m is the magnetic moment of the target, _mx , _my , _mz are the magnetic moment vectors of the target, and the gradient of the three components is simplified by the Kronecker function _δij :

Based on the above formula, assuming that the detection system moves at a constant speed parallel to the x-axis, the above trajectory expression R(t)=R ₀ +v(tt ₀ ) is obtained. Substituting R(t) into the above expression of magnetic induction intensity vector B, and writing the magnetic induction intensity vector B as a linear combination of "Anderson functions", a three-component linear model is obtained, which is expressed as the following calculation model:

The characteristic time quantity τ＝(tt ₀ )v/R ₀ ＝(x ₀ )/R ₀ , f ₁ , f ₂ , f ₃ are Anderson functions, expressed as: f ₁ (τ)＝1/(1+τ ² ) ⁵² , f ₂ (τ)＝τ/(1+τ ² ) ⁵² , f ₃ (τ)＝τ ² /(1+τ ² ) ⁵² , x ₀ is the x-axis coordinate of the target object that is closest to the target object.

For the total field sensor, since the magnitude of the geomagnetic field is much greater than the magnitude of the magnetic anomaly signal, the observed signal can be approximated as follows:

Among them,/>

φ is the local geomagnetic inclination, and β is the local geomagnetic declination. Based on this, the total field magnetic anomaly fingerprint signal expression can be obtained as:

Among them, the μ _x , μ _y and μ _z components represent the projection of the magnetic field strength in three different directions, which are usually aligned with the coordinate axes of the sensor.

Then the vertical gradient of the magnetic anomaly fingerprint signal is expressed as follows:

The Anderson functions f ₄ , f ₅ , and f ₆ are introduced into the vertical gradient of the magnetic anomaly fingerprint signal to simplify it into:

in:

Inversion is performed by combining the three-component linear model and the vertical gradient model of the magnetic anomaly fingerprint signal. The target position x, y, z, magnetic moment m, and _R0 are estimated based on the particle swarm optimization algorithm to obtain the optimal magnetic moment size, target position, and the shortest path CPA between the detection system and the target. The shortest path CPA is pre-configured with an induction angle range, such as 90 degrees to 120 degrees. During the inversion process, the shortest path CPA corresponds to an induction angle θ.

The present application first takes the mean value of the magnetic field data of the target area as T to inversely solve the values of x ₀ , R ₀ , p ₁ , p ₂ and p ₃ , and then uses the ratio of the difference of the magnetometer (i.e., the difference of the magnetic field intensity at two different heights) to the baseline distance as the vertical gradient G _z to solve y and z. Then, the magnetic moment size is calculated based on p ₁ , p ₂ and p ₃ , x, y, and z. The above process uses the particle swarm optimization algorithm to iteratively obtain the optimal magnetic moment size, the position of the target object, and the shortest path CPA between the detection system and the target object.

In the target detection and identification step S3, the sensing interval d is calculated based on the shortest path CPA and the sensing angle θ, and the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve are analyzed, and the constraint relationship between the time-frequency domain characteristics, the magnetic moment size, the sensing interval d and the shortest path or any two of them is combined to determine whether the target is a detection target. The time-domain characteristics in the time-frequency domain characteristics include signal amplitude, signal duration, and wave width. The time-domain waveform of the magnetic anomaly fingerprint signal curve has two forms: single peak and double peak. The frequency domain characteristics in the time-frequency domain characteristics include bandwidth, and the magnetic anomaly fingerprint signal is concentrated in the low-frequency band.

Taking the signal amplitude and wave width in the time domain characteristics as an example, Figure 6 is the time domain signal of two unexploded bombs under the same test conditions. Referring to Figure 6, the test speed is calculated according to the starting point and the end point of the test path. The horizontal axis in the figure is the detection track length, and the vertical axis is the signal strength. In the figure, 60 represents the 60 mortar. It can be seen that the signal amplitude and wave width of the 60 mortar and the twin-linked naval artillery shells in the figure are significantly different under the same test conditions (shortest path CPA, consistent speed). Therefore, it is possible to judge whether the target object is detected based on the signal amplitude and wave width.

In addition, the sensing range and shortest path CPA of the magnetic anomaly fingerprint signal curve of different types of unexploded bombs also have significant differences, which can be used to identify the target. As shown in Figure 8, the embodiment of the present application takes a double-linked naval artillery shell (length 53cm) and a 120mm caliber bomb (length 240cm) as examples for testing, and the relative relationship between the magnetic anomaly signal and the shortest path obtained by inversion during the detection process is displayed in the same way. The horizontal axis of Figure 8 (a)-(b) is the length of the detection trajectory, the lower half of the vertical axis in the figure is the CPA depth, and the upper half of the vertical axis is the signal strength. As shown in Figure 8 (a), the limit test CPA obtained by the target position information acquisition step S2 of the present application is 150cm, and the sensing range d is about 5.1m. When the sensing angle θ∈[90,120], the effective range of the continuous path of the target magnetic anomaly signal is less than 2√3CPA. As shown in Figure 8(b), the limit test CPA of the 120 mm caliber bomb is 550 cm, the sensing interval d is about 15 m, and the sensing interval d, the shortest path CPA and the sensing angle θ are constrained by trigonometric functions.

In some embodiments, the constraint relationship includes: determining the constraint relationship between the time domain feature and the magnetic moment size of the target object and the shortest path CPA, and determining the constraint relationship between the frequency domain feature and the speed and the shortest path CPA. Specifically:

The signal amplitude is proportional to the magnitude of the magnetic moment;

The signal amplitude is inversely proportional to the cube of the shortest path CPA;

The signal duration is proportional to the shortest path CPA.

In addition, according to the time-bandwidth product theorem, the constraint relationship also includes: the bandwidth is proportional to the speed, and the bandwidth is inversely proportional to the shortest path CPA.

The following is an example to illustrate the above constraint relationship.

Taking the proportional relationship between signal amplitude and magnetic moment as an example, refer to Figure 7(a), where the horizontal axis is time and the vertical axis is magnetic field intensity. When the magnetic moments are 1.0Am ² , 1.5Am ² , 2.0Am ² , and 2.5Am ² respectively, as the magnetic moment increases, the amplitude of the magnetic anomaly signal also increases.

Taking the example that the signal amplitude is proportional to the cube of the shortest distance CPA, refer to Figure 7(b), where the horizontal axis is time Times and the vertical axis is magnetic field strength. When the CPA is 1.5m, 2.0m, 2.5m, and 3.0m, respectively, when the magnetic field strength is greater than zero, the signal amplitude increases as the CPA increases, and when the magnetic field strength is less than zero, the signal amplitude increases as the CPA decreases.

Therefore, through the above steps, combined with the analysis of time-frequency domain characteristics, magnetic moment size, induction interval d and shortest path, the embodiment of the present application can more accurately interpret the information hidden in the time-frequency domain characteristics of the magnetic anomaly fingerprint signal, thereby distinguishing different buried unexploded bombs, improving target recognition accuracy, and providing a new means of unexploded bomb detection and identification.

The embodiments of the present application are described and illustrated below through preferred embodiments.

This preferred embodiment uses the Hai 37-55 shell as an example for inversion. The actual burial depth is 20 cm, and the actual magnetic moment of the Hai 37-55 shell is 0.185 A.m2. As shown in Figure 9, the horizontal axis of Figure 9 is time Times, and the vertical axis is the magnetic field intensity. The inversion signal obtained in the inversion process has a high degree of fit with the magnetic anomaly fingerprint curve. As shown in Figure 10, the horizontal axis of Figure 10 is time Times, and the vertical axis is the vertical gradient G. The figure shows that the gradient data obtained in the inversion process has a high degree of fit with the original gradient of the signal. The inversion magnetic moment obtained by inversion is 0.1902 A.m2, which is only 2.8% compared with the actual magnetic moment; the inversion depth is 18 cm, and the minimum error with the actual burial depth is 2 cm. Based on this, the embodiment of the present application can more accurately invert the magnetic moment and depth information of the buried small target by introducing the vertical gradient amount and the particle swarm optimization algorithm, which provides higher accuracy and reliability for the detection and identification of unexploded bombs.

Correspondingly, as shown in FIG11 , the horizontal axis of FIG11 is the X-axis coordinate and the vertical axis is the Y-axis coordinate. The figure shows that the inversion position obtained in the inversion process is close to the actual position of the buried object, and the buried object can be accurately located.

In another embodiment, to facilitate the prediction and analysis of the size and type of the target, the embodiment of the present application can obtain the target magnetic moment size, target position, shortest path CPA, sensing interval and time-frequency domain characteristics obtained by batch inversion based on the different types of unexploded bombs and their different magnetic anomaly fingerprint signals under different test environments, and establish a database by arbitrarily combining the relative relationship between the target magnetic moment size, target position, shortest path CPA, sensing interval and the time-frequency domain characteristics, so as to associate and form a knowledge graph or train to form a prediction model, so as to automatically locate the target and predict the size and type of the target.

In the embodiment of the present application, magnetic anomaly fingerprint signals of different types of unexploded bombs are collected in advance, and these signals are obtained by field measurement on different environments and different types of targets. Based on the target magnetic moment size, target position, shortest path CPA, induction interval and time-frequency domain characteristics obtained by analysis according to the batch inversion of the present application, a knowledge graph is constructed on the basis of a database, and these parameters and their relative relationships are compiled into the graph, and the relative relationships are configured based on the constraints of the present application; the data in the database are used to train a machine learning model, such as a decision tree, random forest or neural network, so that the model can predict the type and size of the target according to the magnetic moment size, position, CPA, induction interval and time-frequency domain characteristics of the target, thereby realizing the effective use of magnetic data to automatically locate the target and predict its detailed characteristics, which has important application value in many fields such as geological exploration, archaeology and safety inspection.

It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims (10)

1. A method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis, characterized by comprising: The magnetic anomaly fingerprint acquisition step uses a magnetic field sensor to collect magnetic field data of the target area, performs differential processing, and extracts a magnetic anomaly fingerprint signal; The target position information acquisition step is to treat the target as a magnetic dipole, and to obtain the magnetic moment size of the target, the target position, and the shortest path CPA between the detection system and the target by using a particle swarm optimization algorithm based on the magnetic dipole model, the magnetic anomaly fingerprint signal, and the trajectory data of the magnetic field sensor; The target object detection and identification step calculates the sensing interval d based on the shortest path and the sensing angle θ, and analyzes the time-frequency domain characteristics of the magnetic anomaly fingerprint signal curve, and combines the time-frequency domain characteristics, the magnetic moment size, the sensing interval d and the shortest path or the constraint relationship between any one or two to determine whether the target object is a detection target. 2. The buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 1 is characterized in that the magnetic anomaly fingerprint acquisition step further comprises: A data processing step, fitting and mapping the magnetic field data collected by the magnetic field sensor with the trajectory data, and converting the geographic coordinates of the data into a Cartesian coordinate system; The target data extraction step is to extract the magnetic field data of the target area and the corresponding trajectory data from the converted data, wherein the trajectory data is expressed as: R(t)=R ₀ +v(tt ₀ ), R ₀ is used to indicate that the magnetic field sensor is at the 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 when the magnetic field sensor is closest to the target object. 3. The buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 2 is characterized in that the target object position information acquisition step further comprises: Bring the trajectory data into the gradient tensor of the magnetic dipole model, and express the magnetic induction intensity vector B as a three-component linear model; The three-component linear model and the vertical gradient model of the magnetic anomaly fingerprint signal are combined for inversion, and the target position x, y, z, magnetic moment m, and _R0 are estimated based on the particle swarm optimization algorithm to obtain the optimal magnetic moment size, target position, and the shortest path CPA between the detection system and the target. 4. The buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 3 is characterized in that the three-component linear model is expressed as the following calculation model: Where _μ0 is the magnetic permeability, x, y, z are the coordinates of the target object's position, m _x , my _y , m _z are the magnetic moment vectors of the target object, the characteristic time quantity τ = (tt ₀ )v/R ₀ = (xx ₀ )/R ₀ , f ₁ , f ₂ , f ₃ are Anderson functions, expressed as: f ₁ (τ) = 1/(1+τ ² ) ^5/2 , f ₂ (τ) = τ/(1+τ ² ) ^5/2 , f ₃ (τ) = τ ² /(1+τ ² ) ^5/2 . 5. The buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 4 is characterized in that the vertical gradient model is represented by the following calculation model: in, φ is the local magnetic inclination, and β is the local magnetic declination. 6. According to the buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis according to claim 2, the time domain features in the time-frequency domain features include signal amplitude, signal duration, and wave width, and the frequency domain features in the time-frequency domain features include frequency bandwidth. 7. According to the method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis according to claim 6, the constraint relationship includes: determining the constraint relationship between time domain characteristics and the magnetic moment size of the target object and the shortest path CPA, and determining the constraint relationship between frequency domain characteristics and the speed and the shortest path CPA. 8. According to the buried unexploded bomb detection and identification method based on magnetic anomaly fingerprint curve analysis of claim 7, the signal amplitude is proportional to the magnetic moment size, the signal amplitude is inversely proportional to the cube of the shortest path CPA, and the signal duration is proportional to the shortest path CPA. 9. According to the method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis according to claim 7, the bandwidth is directly proportional to the speed, and the bandwidth is inversely proportional to the shortest path CPA. 10. The method for detecting and identifying buried unexploded bombs based on magnetic anomaly fingerprint curve analysis according to any one of claims 1 to 9 is characterized in that, based on the differences in magnetic anomaly fingerprint signals of different types of unexploded bombs and under different test environments, the target magnetic moment size, target position, shortest path CPA, induction interval and time-frequency domain characteristics obtained by batch inversion are obtained, and a database is established by arbitrarily combining the relative relationship between the target magnetic moment size, target position, shortest path CPA, induction interval and the time-frequency domain characteristics.