Method for detecting and identifying sub-meter buried unexploded ordnance based on correlation three-dimensional magnetic imaging

Abstract

The invention provides a sub-meter buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging, and the method comprises the steps: obtaining ground surface magnetic anomaly measured data and corresponding trajectory data of a detection region, and dividing a local anomaly region according to a magnetic anomaly change rate; dividing the underground space corresponding to the local abnormal region into three-dimensional grids based on the local abnormal region, calculating the theoretical magnetic anomaly of each grid node based on a magnetic dipole model, obtaining a normalized local correlation coefficient point by point with the measured data of the magnetic anomaly, and reserving nodes higher than a preset threshold as candidate nodes; taking error minimization between theoretical magnetic anomaly of the candidate node and magnetic anomaly measured data as a fitness function, and obtaining an optimal magnetization parameter and an optimal local correlation imaging coefficient based on particle swarm optimization iterative optimization; and based on the optimal local correlation imaging coefficient, carrying out integration according to a spatial position, generating a three-dimensional magnetic imaging result of the detection area, and obtaining a contour parameter and a spatial position of the sub-meter buried unexploded ordnance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of magnetic anomaly detection, more particularly, the present application relates to a sub-meter buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging. BACKGROUND

[0002] Magnetic anomaly detection is an important means of geophysical exploration, which identifies underground magnetic anomaly bodies by measuring the spatial variation of the ground magnetic field, and has been widely used in mineral exploration, shipwreck search and rescue, and unexploded ordnance (UXO) detection. The existing technology mainly focuses on the "signal detection" level of the magnetic target, and usually uses amplitude, wave width or magnetic moment information for rough classification, which is difficult to obtain the accurate profile parameters of sub-meter targets. For sub-meter unexploded ordnance buried underground, the traditional method is limited by insufficient resolution, large background noise, unknown magnetization direction and other factors, which cannot effectively distinguish unexploded ordnance from fragments, resulting in high risk, low efficiency and high cost of explosive ordnance disposal.

[0003] At present, three-dimensional magnetic imaging technology has been applied in the field of geological exploration, but it is mainly aimed at large-scale ore bodies or geological structures, with sparse grid and simple model, and lacks special optimization for sub-meter small targets. In addition, the existing inversion algorithm mostly uses linear or gradient method, which is easy to fall into local extremum and sensitive to initial value, and it is difficult to accurately invert the target position, size and magnetization direction at the same time.

[0004] Therefore, there is an urgent need for a sub-meter buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging, which can adapt to the characteristics of sub-meter small targets, realize accurate three-dimensional profile imaging and key physical property parameter inversion, and effectively distinguish unexploded ordnance from fragments, so as to realize accurate detection and identification of sub-meter buried unexploded ordnance. SUMMARY

[0005] The present application provides a sub-meter buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging, which at least solves the problems of insufficient imaging accuracy, missing physical property parameters and difficult target differentiation in the existing sub-meter buried unexploded ordnance detection and identification method.

[0006] To achieve the above purpose, the present application provides a sub-meter buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging, comprising: Data acquisition step: acquiring the ground magnetic anomaly measured data and corresponding trajectory data of the detection area, and dividing the local anomaly area according to the magnetic anomaly rate; Correlation coefficient calculation step: dividing the corresponding underground space of the local anomaly area into three-dimensional grid based on the local anomaly area, calculating the theoretical magnetic anomaly of each grid node based on the magnetic dipole model, and obtaining the normalized local correlation coefficient with the measured data point by point, and retaining the nodes higher than the preset threshold as candidate nodes; Parameter optimization and inversion steps: Using the minimum error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node as the fitness function, the optimal magnetization parameters and the optimal local correlation imaging coefficients are obtained by iterative optimization based on the particle swarm optimization algorithm. Three-dimensional imaging and target recognition steps: Based on the optimal local correlation imaging coefficients, the three-dimensional magnetic imaging results of the detection area are generated by integrating them according to spatial location, and the contour parameters and spatial location of the buried unexploded ordnance at the sub-meter level are obtained.

[0007] Furthermore, the correlation coefficient calculation step includes: Based on the local anomaly region, the corresponding underground space is divided into a three-dimensional grid, and each grid node corresponds to a hypothetical uniformly magnetized dipole. Based on the magnetic dipole model, the theoretical magnetic anomaly sequence of each grid node is calculated according to the grid node coordinates, geomagnetic field dip angle, geomagnetic field deflection angle and initial magnetization parameters. Based on the normalized cross-correlation coefficient formula, the local correlation coefficients of the theoretical magnetic anomaly sequence and the measured magnetic anomaly data are calculated point by point to obtain the normalized local correlation coefficient of each node. Set a correlation coefficient threshold and retain grid nodes whose normalized local correlation coefficient is greater than or equal to the threshold as candidate nodes.

[0008] Furthermore, the parameter optimization and inversion step includes: The fitness function is the normalized mean square error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate nodes. The magnetization tilt angle, magnetization deflection angle, and local correlation imaging coefficients are encoded as multidimensional particle positions in the particle swarm algorithm. In each iteration of the particle swarm optimization algorithm, the theoretical magnetic anomaly is recalculated based on the magnetic dipole model, and the particle velocity and position are updated until the fitness function converges or the preset maximum number of iterations is reached. Output the optimal magnetization parameter and optimal local correlation imaging coefficient corresponding to the globally optimal particle position after convergence.

[0009] Furthermore, the three-dimensional imaging and target recognition steps include: Based on the optimal local correlation imaging coefficients, a three-dimensional imaging matrix is ​​obtained by mapping the spatial location to a three-dimensional grid in the detection area. Threshold segmentation is performed on the three-dimensional imaging matrix to obtain the contour parameters and spatial coordinates of the buried unexploded ordnance at the sub-meter level, and the spatial position is output after being correlated with the trajectory data.

[0010] Furthermore, the data acquisition step includes: Based on airborne magnetic survey equipment, the measured data of surface magnetic anomalies are continuously collected at preset measurement point intervals, and the trajectory data is recorded simultaneously. A regular measurement network corresponding to the measured magnetic anomaly data is formed by interpolation.

[0011] This invention provides a sub-meter-level detection and identification system for buried unexploded ordnance based on correlation-based three-dimensional magnetic imaging. The system applies the aforementioned sub-meter-level detection and identification method for buried unexploded ordnance based on correlation-based three-dimensional magnetic imaging, and is characterized by comprising: Data acquisition module: acquires the measured magnetic anomaly data and corresponding trajectory data of the detection area, and divides the local anomaly area according to the rate of change of magnetic anomaly; The correlation coefficient calculation module divides the corresponding underground space into a three-dimensional grid based on the local anomaly area, calculates the theoretical magnetic anomaly of each grid node based on the magnetic dipole model, and obtains the normalized local correlation coefficient point by point with the measured magnetic anomaly data, retaining nodes higher than the preset threshold as candidate nodes. Parameter optimization and inversion module: Using the minimum error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node as the fitness function, the module iteratively optimizes based on the particle swarm optimization algorithm to obtain the optimal magnetization parameters and the optimal local correlation imaging coefficients. 3D Imaging and Target Recognition Module: Based on the optimal local correlation imaging coefficients, the module integrates them according to spatial location to generate 3D magnetic imaging results of the detection area, and obtains the contour parameters and spatial location of the buried unexploded ordnance at the sub-meter level.

[0012] Furthermore, the correlation coefficient calculation module includes: Based on the local anomaly region, the corresponding underground space is divided into a three-dimensional grid, and each grid node corresponds to a hypothetical uniformly magnetized dipole. Based on the magnetic dipole model, the theoretical magnetic anomaly sequence of each grid node is calculated according to the grid node coordinates, geomagnetic field dip angle, geomagnetic field deflection angle and initial magnetization parameters. Based on the normalized cross-correlation coefficient formula, the correlation coefficient between the theoretical magnetic anomaly sequence and the measured magnetic anomaly data is calculated point by point to obtain the normalized local correlation coefficient of each node. Set a correlation coefficient threshold and retain grid nodes whose normalized local correlation coefficient is greater than or equal to the threshold as candidate nodes.

[0013] Furthermore, the parameter optimization and inversion module includes: The fitness function is the normalized mean square error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate nodes. The magnetization tilt angle, magnetization deflection angle, and local correlation imaging coefficients are encoded as multidimensional particle positions in the particle swarm algorithm. In each iteration of the particle swarm optimization algorithm, the theoretical magnetic anomaly is recalculated based on the magnetic dipole model, and the particle velocity and position are updated until the fitness function converges or the preset maximum number of iterations is reached. Output the optimal magnetization parameter and optimal local correlation imaging coefficient corresponding to the globally optimal particle position after convergence.

[0014] The present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned sub-meter level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging.

[0015] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned sub-meter level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention uses a related imaging algorithm to directly convert the normalized correlation coefficient between the measured magnetic anomaly data and the dipole theoretical response into the contour imaging coefficient of sub-meter-level buried small targets. This allows the dimensional errors of the target length, width, and height to be controlled within the centimeter level, significantly improving the contour recognition accuracy of sub-meter-level buried small targets. This is superior to traditional methods that can only provide meter-level fuzzy estimates.

[0017] This invention utilizes a particle swarm optimization algorithm to simultaneously optimize magnetization tilt angle, deflection angle, and imaging coefficients, achieving robust inversion under weak signal conditions. This not only significantly reduces spatial positioning errors but also obtains a magnetization fingerprint that can be used to distinguish between projectiles and fragments. The invention outputs three-dimensional visualization results in real time on-site, automatically distinguishing between highly correlated (unexploded ordnance) and low-correlation fragment targets in the results without additional prior knowledge. This greatly shortens the search and disposal time and reduces the risk of bomb disposal.

[0018] This invention has the advantage of being highly adaptable to sub-meter-level small target detection scenarios. On the one hand, it focuses on key anomaly areas by calculating local correlation coefficients, avoiding the obscuring of small targets by background signals in global imaging. On the other hand, the combination of this algorithm with particle swarm optimization can dynamically adjust magnetization parameters and imaging coefficients, overcoming the defect of traditional linear inversion being prone to getting trapped in local extrema. This ensures that even in sub-meter-level target detection with weak signals and strong interference, it can still output highly reliable correlation imaging results, which not only improves detection efficiency but also provides accurate and reliable target information for bomb disposal operations, significantly reducing the risks and costs of actual search and disposal work. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of the sub-meter level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging in an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the sub-meter level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging in an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the sub-meter level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging in an embodiment of the present invention; Figure 4 This is a schematic diagram of three-dimensional spatial discretization based on the magnetic dipole model in an embodiment of the present invention; Figure 5 This is a schematic diagram of the model's spatial location in an embodiment of the present invention; Figure 6 This is a magnetic field cloud map of the detection surface in an embodiment of the present invention; Figure 7 This is a schematic diagram of the inversion results in an embodiment of the present invention; Figure 8 This is a schematic diagram of a three-dimensional slice in an embodiment of the present invention; Figure 9 This is a schematic diagram of the XY, YZ, and XZ cross-sections in an embodiment of the present invention; Figure 10 This is a schematic diagram of the experimental test scenario setup in an embodiment of the present invention; Figure 11 This is a schematic diagram of the original magnetic anomaly signal in an embodiment of the present invention; Figure 12 This is a cloud map showing the magnetic field trajectory of the experimental area in this embodiment of the invention. Figure 13 This is a schematic diagram of the three-dimensional imaging results of experimental data in an embodiment of the present invention; Figure 14 This is a schematic diagram of a three-dimensional imaging XY surface slice of experimental data in an embodiment of the present invention; Figure 15 This is a schematic diagram of the orthogonal tangent plane for contour parameter inversion of targets T1, T2, and T3 in an embodiment of the present invention; Figure 16 This is a schematic diagram of the sub-meter level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging in an embodiment of the present invention; Figure 17 This is a schematic diagram of a computer device provided in an embodiment of the present invention.

[0020] In the above image: 100. Data Acquisition Module; 200. Correlation Coefficient Calculation Module; 300. Parameter Optimization and Inversion Module; 400. 3D Imaging and Target Recognition Module; 40. via bus; 41. processor; 42. memory; 43. communication interface. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0022] In the description of this application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "level," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0024] like Figures 1-17 As shown, this invention provides a sub-meter-level detection and identification method for buried unexploded ordnance based on correlation three-dimensional magnetic imaging. The following is a combination of... Figures 1-17 This paper details the sub-meter-level detection and identification method for buried unexploded ordnance based on correlation-based three-dimensional magnetic imaging.

[0025] Example 1: like Figures 1-3 As shown, the present invention provides a sub-meter level detection and identification method for buried unexploded ordnance based on correlation three-dimensional magnetic imaging, including a data acquisition step S100, a correlation coefficient calculation step S200, a parameter optimization and inversion step S300, and a three-dimensional imaging and target identification step S400.

[0026] Data acquisition step S100: Acquire the measured data of the surface magnetic anomaly in the detection area and the corresponding trajectory data, and divide the local anomaly area according to the rate of change of the magnetic anomaly.

[0027] Correlation coefficient calculation step S200: Based on the local anomaly area, the corresponding underground space is divided into a three-dimensional grid. The theoretical magnetic anomaly of each grid node is calculated based on the magnetic dipole model. The normalized local correlation coefficient is obtained point by point with the measured magnetic anomaly data. Nodes with a value higher than the preset threshold are retained as candidate nodes.

[0028] Parameter optimization and inversion step S300: Using the minimum error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node as the fitness function, the optimal magnetization parameters and the optimal local correlation imaging coefficients are obtained through iterative optimization based on the particle swarm optimization algorithm.

[0029] 3D imaging and target recognition step S400: Based on the optimal local correlation imaging coefficients, integrate them according to spatial location to generate 3D magnetic imaging results of the detection area, and obtain the contour parameters and spatial location of the sub-meter level buried unexploded ordnance.

[0030] Preferably, the data acquisition step S100 includes: Based on airborne magnetic survey equipment, the measured data of surface magnetic anomalies are continuously collected at preset measurement point intervals, and the trajectory data is recorded simultaneously. A regular measurement network corresponding to the measured magnetic anomaly data is formed by interpolation.

[0031] In some embodiments, such as Figure 2 As shown, in the data acquisition step S100, the measured data of the surface magnetic anomaly in the detection area and the corresponding trajectory data are acquired by a magnetic detection device equipped with a fluxgate sensor, attitude sensor, lidar, and dual optical pump. The raw data of the total surface magnetic anomaly field and gradient field of the detection area are continuously collected at preset measurement point intervals by flying or moving the survey line. At the same time, the precise trajectory coordinates and attitude data provided by the integrated navigation system are recorded. The lidar simultaneously acquires the surface elevation information to assist in the construction of a digital elevation model and to accurately correct the detection height. After the raw data is preprocessed by denoising, diurnal variation correction, and attitude compensation, the measured data grid of magnetic anomaly with precise geographic coordinates corresponding to the regular survey network is generated by interpolation. Based on the spatial change rate of the grid data, potential local anomaly areas are automatically divided, providing a data basis for subsequent related imaging calculations.

[0032] Preferably, such as Figures 3-4 As shown, the correlation coefficient calculation step S200 includes: Based on the local anomaly region, the corresponding underground space is divided into a three-dimensional grid, and each grid node corresponds to a hypothetical uniformly magnetized dipole. Based on the magnetic dipole model, the theoretical magnetic anomaly sequence of each grid node is calculated according to the grid node coordinates, geomagnetic field dip angle, geomagnetic field deflection angle and initial magnetization parameters. Based on the normalized cross-correlation coefficient formula, the local correlation coefficients of the theoretical magnetic anomaly sequence and the measured magnetic anomaly data are calculated point by point to obtain the normalized local correlation coefficient of each node. Set a correlation coefficient threshold and retain grid nodes whose normalized local correlation coefficient is greater than or equal to the threshold as candidate nodes.

[0033] In some embodiments, such as Figure 4 As shown, the underground space to be measured is divided into spatial grids based on the detection plane. Assuming any point under the measurement area is (xp, yp, zp), the magnetization intensity of p uniformly magnetized spheres with volume v is Jp, and the magnetic moment is mp = Jp.

[0034] Using the local anomaly region as the detection plane, the corresponding underground space is divided into a spatial grid. This imaging space Li × Wi plane is bounded by the detection region L × W where Li ≤ L and Wi ≤ W. Its vertical range Hi varies depending on the application scenario; buried unexploded ordnance rarely exceeds 5 m. Therefore, the p-th dipole of any point i (Xi, Yi, Zi) on the magnetic anomaly field ∆Bi(xp, yp, zp) sensing plane can be represented as: (1) in, Let be the vacuum permeability, r be the distance vector from grid node p to measurement point i, r be its magnitude, and A, B, C, D, E, and F be geometric coefficients related to the relative geometric positions of the measurement point and the source point, and their specific definitions are as follows: (2) Among them, the inclination angle of the geomagnetic field is I0, the deflection angle of the geomagnetic field is D0, the inclination angle of the magnetization is I1, and the deflection angle of the magnetization is D1.

[0035] The correlation coefficient Cp between the measured magnetic anomaly ∆T at i (Xi, Yi, Zi) and the corresponding theoretical response ∆Bi is defined as: (3) Assuming Jp>0, then mp>0. Substituting equation (1) into equation (3), we obtain the normalized local correlation coefficient Cp' as follows: (4) Where Cp' represents the normalized correlation between the measured anomaly and the basis function of the p-th dipole, and ΔBi represents the basis function of the p-th dipole.

[0036] ΔBi' represents the normalized basis function of the p-th dipole, as follows: (5) According to the Cauchy-Schwarz inequality: (6) Therefore, the correlation coefficient Cp' in Equation (4) ranges from [-1, 1]. Cp' characterizes the degree of correlation between the measured anomaly and the dipole basis function. A larger Cp' value indicates a higher probability of the dipole, while a negative value indicates a lower probability. The subsurface imaging space is discretized into a uniform grid structure, and the correlation coefficients of all nodes are calculated according to Equation (4). These correlation coefficients then characterize the spatial distribution of the equivalent physical parameters associated with the subsurface anomaly.

[0037] Preferably, the parameter optimization and inversion step S300 includes: The normalized mean square error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node is used as the fitness function. The magnetization tilt angle, magnetization deflection angle, and local correlation imaging coefficients are encoded as multidimensional particle positions in the particle swarm algorithm. In each iteration of the particle swarm optimization algorithm, the theoretical magnetic anomaly is recalculated based on the magnetic dipole model, and the particle velocity and position are updated until the fitness function converges or the preset maximum number of iterations is reached. Output the optimal magnetization parameter and optimal local correlation imaging coefficient corresponding to the globally optimal particle position after convergence.

[0038] In some embodiments, the parameter optimization inversion step employs the Particle Swarm Optimization (PSO) algorithm, a global optimization tool based on swarm intelligence that seeks the optimal solution in a high-dimensional search space by simulating the social behavior of flocks of birds or schools of fish.

[0039] In some embodiments, assuming the search space is n-dimensional, a particle i in the swarm can be represented by an n-dimensional vector xi = (xi1, xi2, xi3, ..., xin). The velocity of the particle can be represented by vi = (vi1, vi2, vi3, ..., vin). The fitness of each particle is evaluated according to the objective function of the optimization problem. The best position previously visited by particle i is recorded as its individual best position, pi = (pi1, pi2, pi3, ..., pin). The position of the best individual in the entire swarm is recorded as the global best position g = (g1, g2, g3, ..., gn). In each step, the particle's velocity and its new position are assigned according to the following two equations: (7) (8) Where w is the inertia weight, used to control the influence of the particle's previous velocity on its current velocity. r1 and r2 are independent, uniformly distributed random variables in the range [0,1]. c1 and c2 are used to control the maximum step size. In general applications, c1 = c2 = 1.5.

[0040] (9) Where Tmax represents the maximum number of generations, wmin represents the minimum inertia weight, wmax represents the maximum inertia weight, and t represents the current iteration number. In most applications, wmax = 0.9 and wmin = 0.4. This strategy allows the algorithm to focus on global exploration in the early stages and on fine-grained local search in the later stages.

[0041] The fitness function is defined as the normalized mean square error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate nodes, as follows: (10) Preferably, the three-dimensional imaging and target recognition step S400 includes: A three-dimensional imaging matrix is ​​obtained by mapping the optimal local correlation imaging coefficients to a three-dimensional grid in the detection area according to their spatial location. Threshold segmentation is performed on the three-dimensional imaging matrix to obtain the contour parameters and spatial coordinates of the buried unexploded ordnance at the sub-meter level, and the spatial position is output after being correlated with the trajectory data.

[0042] In some embodiments, the optimal local correlation imaging coefficients obtained by inversion based on the particle swarm optimization algorithm are mapped and integrated according to their corresponding three-dimensional grid node spatial coordinates to generate a three-dimensional physical property imaging matrix covering the entire detection area. The three-dimensional imaging matrix is ​​segmented using an adaptive threshold segmentation algorithm or a fixed threshold based on empirical values ​​to extract highly correlated imaging units. The geometric contour parameters of the buried unexploded ordnance at the sub-meter level are obtained through cluster analysis and a three-dimensional contour tracking algorithm, including its length L, width W, and height H. Then, through spatial coordinate transformation, the coordinates of the center point of the target contour are accurately correlated with its corresponding surface trajectory data and elevation information, and finally the precise spatial location of the unexploded ordnance in the real geographic coordinate system is output.

[0043] In some embodiments, such as Figures 5-9 As shown, a systematic simulation experiment was conducted to verify the effectiveness of the sub-meter level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging proposed in this invention.

[0044] The specific parameter configuration of the simulation environment is shown in Table 1. The three-dimensional boundary of the simulated detection space is set as follows: X-axis direction [-5, 5] meters, Y-axis direction [-5, 5] meters, Z-axis depth range [-8, 0] meters. Figure 5As shown, the space is discretized into 10×10×8 grid cells, meaning the total number of grid cells is n×m×l = [10×10×8]m³. The magnetization intensity of the simulated target is set to Jp = 1 A / m, and the actual geomagnetic parameters of a certain city are used as the background field: geomagnetic field dip angle... Geomagnetic field declination .

[0045]

[0046] Table 1 Simulation Environment Parameter Configuration Table A simulated unexploded ordnance target with dimensions (Lt, Wt, Ht) = (1.11, 1.11, 2.29) meters was pre-set in the simulation space, with its center's real-world spatial coordinates being (xt, yt, zt) = (-1.11, -1.11, -3.43) meters. The sensor's detection height was set at Hf = 0.5 meters above the ground surface.

[0047] Based on the above parameters, the theoretical magnetic anomaly field on the sensing plane is first obtained through forward modeling. For example... Figure 6 As shown, the magnetic field value was mapped onto the detection trajectory, generating a magnetic field anomaly cloud map in the XY plane. The map clearly shows a local magnetic anomaly signal with an amplitude of approximately 20 nT.

[0048] The magnetic anomaly data obtained from forward modeling is used as input to the algorithm to execute the correlation imaging and PSO optimization inversion process described in this invention. The resulting three-dimensional imaging results are as follows: Figure 7 and Figure 8 As shown. Figure 9 As shown, the outline and spatial location of the target were successfully reconstructed through 3D visualization and slice analysis of the inverted data. The inverted target size is (L, W, H) = (1.11, 1.11, 2.29) meters, which is completely consistent with the preset true size; the inverted target center position is (x_i, y_i, z_i) = (-1.31, -1.31, -3.78) meters, and the positioning error is small compared with the true position (Δx=0.20m, Δy=0.20m, Δz=0.35m), which fully verifies the effectiveness and reliability of the method of the present invention in accurately identifying the outline and spatially locating buried targets at the sub-meter scale.

[0049] In some embodiments, such as Figures 10-15 As shown, the sub-meter level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging of the present invention was verified by field experiments to evaluate its performance and target identification capability in real complex environments.

[0050] The experiment employed the TRU100-25 airborne magnetic detection system. This system integrates a signal acquisition module, a data transmission module, a high-precision positioning module, a laser altimeter, and the core rubidium optically pumped gradient magnetometer. The system features low power consumption (≤8W), a high sampling rate (20Hz), and high sensitivity (low background noise as low as 10 pT / √Hz@1Hz), making it suitable for high-precision magnetic anomaly detection missions.

[0051] like Figure 10 As shown, three typical targets were deployed at the test site: one mortar shell as target T2, and two shrapnel fragments as targets T1 and T3, respectively. The actual parameters of each target are as follows: Target T1 (shrapnel 1) is located on survey line L2, with actual dimensions of length L = 0.25 m, width W = 0.15 m, and height H = 0.08 m. Target T2 (85mm mortar) is also located on survey line L2, with actual dimensions of L = 0.95 m, W = 0.10 m, and H = 0.10 m. Target T3 (shrapnel 2) is located between survey lines L4 and L5, with actual dimensions of L = 0.35 m, W = 0.25 m, and H = 0.05 m. The burial depth of all three targets is D = -0.25 m.

[0052] The system collects the raw magnetic anomaly signals, such as Figure 11 As shown, target T1 produces an anomaly of approximately 90 nT on measurement line L2, T2 produces a significant anomaly on measurement lines L1-L3 with a maximum amplitude of approximately 830 nT, and T3 produces an anomaly of approximately 110 nT on measurement lines L4 and L5.

[0053] The regional magnetic field trajectory mapping cloud map obtained after interpolating the original data is as follows: Figure 12 As shown, although the cloud map can clearly show the three abnormal areas and preliminarily locate their planar positions, it cannot provide depth information and target outline dimensions, making it difficult to distinguish between unexploded ordnance and shrapnel.

[0054] The method of this invention was applied to this set of data. After processing, the following results were successfully obtained: Figure 13 The three-dimensional imaging results of the three targets shown are as follows: Figure 14 The XY surface slice is shown. Three-dimensional slice analysis was performed on the imaging results. Figure 15 In the diagram, (a), (b), and (c) correspond to the YZ, XY, and XZ orthogonal tangents of targets T1, T2, and T3, respectively.

[0055] The outline dimensions and spatial positions of each target are as follows: The dimensions of T1 are: true dimensions (L, W, H) = (0.25, 0.15, 0.08), image dimensions (L, W, H) = (0.45, 0.20, 0.20), and errors (eL, eW, eH) = (0.20, 0.05, 0.12). The spatial positions are: true position (x, y, z) = (-2.40, 5.60, -0.25), image position (x, y, z) = (-2.20, 5.28, -0.38), and errors (ex, ey, ez) = (0.20, -0.32, -0.13).

[0056] The dimensions of T2 are: true dimensions (L, W, H) = (0.95, 0.10, 0.10), image dimensions (L, W, H) = (1.15, 0.18, 0.20), and errors (eL, eW, eH) = (0.20, 0.08, 0.10). The spatial positions are: true position (x, y, z) = (-1.86, -1.47, -0.25), image position (x, y, z) = (-1.69, -1.51, -0.38), and errors (ex, ey, ez) = (0.17, -0.04, -0.13).

[0057] The dimensions of T3 are as follows: Actual dimensions (L, W, H) = (0.35, 0.25, 0.05), Imaging dimensions (L, W, H) = (0.41, 0.38, 0.20), Errors (eL, eW, eH) = (0.06, 0.13, 0.15). The spatial position results are as follows: Actual position (x, y, z) = (1.54, -3.69, -0.25), Imaging position (x, y, z) = (2.04, -3.76, -0.38), Errors (ex, ey, ez) = (0.50, -0.07, -0.13), all in meters (m).

[0058] Regarding the volume parameters, the errors are as follows: eLmax = 0.20 m, eWmax = 0.13 m, eHmax = 0.15 m. The positional errors are exmax = 0.50 m, eymax = 0.32 m, ezmax = 0.13 m.

[0059] Experimental results show that the method of this invention can effectively invert the spatial location and geometric contour of buried targets from magnetic anomaly data. Although there are errors between the inverted dimensions and the true values ​​(maximum error in length 0.20 m, maximum error in width 0.13 m, maximum error in height 0.15 m), and the location positioning errors (maximum 0.50 m in the X direction, maximum 0.32 m in the Y direction, and maximum 0.13 m in the Z direction) are all within the sub-meter range. More importantly, the inverted target volume parameters show significant differences between the mortar shell (T2) and the two fragments (T1, T3). This provides a crucial technical basis for distinguishing between threatening unexploded ordnance and harmless metal fragments in actual bomb disposal operations, verifying the feasibility and practical value of the method of this invention.

[0060] Example 2: like Figure 16 As shown, the present invention provides a sub-meter level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging, including a data acquisition module 100, a correlation coefficient calculation module 200, a parameter optimization and inversion module 300, and a three-dimensional imaging and target identification module.

[0061] Data acquisition module 100: acquires the measured magnetic anomaly data and corresponding trajectory data of the detection area, and divides the local anomaly area according to the rate of change of magnetic anomaly; Correlation coefficient calculation module 200: Based on the local anomaly area, the corresponding underground space is divided into a three-dimensional grid. The theoretical magnetic anomaly of each grid node is calculated based on the magnetic dipole model. The normalized local correlation coefficient is obtained point by point with the measured magnetic anomaly data. Nodes with a value higher than the preset threshold are retained as candidate nodes. Parameter optimization and inversion module 300: Using the minimum error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node as the fitness function, it iteratively optimizes based on the particle swarm algorithm to obtain the optimal magnetization parameters and the optimal local correlation imaging coefficients. 3D Imaging and Target Recognition Module 400: Based on the optimal local correlation imaging coefficients, it integrates them according to spatial location to generate 3D magnetic imaging results of the detection area and obtain the contour parameters and spatial location of the buried unexploded ordnance at the sub-meter level.

[0062] Preferably, the correlation coefficient calculation module 200 includes: Based on the local anomaly region, the corresponding underground space is divided into a three-dimensional grid, and each grid node corresponds to a hypothetical uniformly magnetized dipole. Based on the magnetic dipole model, the theoretical magnetic anomaly sequence of each grid node is calculated according to the grid node coordinates, geomagnetic field dip angle, geomagnetic field deflection angle and initial magnetization parameters. Based on the normalized cross-correlation coefficient formula, the correlation coefficient between the theoretical magnetic anomaly sequence and the measured magnetic anomaly data is calculated point by point to obtain the normalized local correlation coefficient of each node. Set a correlation coefficient threshold and retain grid nodes whose normalized local correlation coefficient is greater than or equal to the threshold as candidate nodes.

[0063] Preferably, the parameter optimization and inversion module 300 includes: The normalized mean square error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node is used as the fitness function. The magnetization tilt angle, magnetization deflection angle, and local correlation imaging coefficients are encoded as multidimensional particle positions in the particle swarm algorithm. In each iteration of the particle swarm optimization algorithm, the theoretical magnetic anomaly is recalculated based on the magnetic dipole model, and the particle velocity and position are updated until the fitness function converges or the preset maximum number of iterations is reached. Output the optimal magnetization parameter and optimal local correlation imaging coefficient corresponding to the globally optimal particle position after convergence.

[0064] Example 3: Combination Figure 17 As shown, this embodiment discloses a specific implementation of a computer device. The computer device may include a processor 41 and a memory 42 storing computer program instructions.

[0065] Specifically, the processor 41 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0066] The memory 42 may include a mass storage device for data or instructions. For example, and not limitingly, the memory 42 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 42 may include removable or non-removable (or fixed) media. Where appropriate, the memory 42 may be internal or external to a data processing device. In a particular embodiment, the memory 42 is non-volatile memory. In a particular embodiment, the memory 42 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM can be a mask-programmed ROM, a programmable read-only ROM (PROM), an erasable programmable read-only ROM (EPROM), an electrically erasable programmable read-only ROM (EEPROM), or an electrically alterable read-only ROM. RAM can be Memory (EAROM) or Flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM), where DRAM can be Fast Page Mode Dynamic Random Access Memory (FPMDRAM), Extended Data Out Dynamic Random Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0067] The memory 42 can be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 41.

[0068] The processor 41 reads and executes the computer program instructions stored in the memory 42 to implement the sub-meter level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging in the above embodiment.

[0069] In some embodiments, the computer device may further include a communication interface 43 and a bus 40. For example, Figure 17 As shown, the processor 41, memory 42, and communication interface 43 are connected through bus 40 and complete communication with each other.

[0070] Communication interface 43 is used to enable communication between modules, devices, units and / or equipment in the embodiments of this application.

[0071] Communication port 43 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0072] Bus 40 includes hardware, software, or both, that couples components of a computer device together. Bus 40 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 40 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, and a Serial Advanced Technology Accessory (SEL). The bus may be a Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 40 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0073] Furthermore, in conjunction with the sub-meter-level detection and identification method for buried unexploded ordnance based on correlation-based three-dimensional magnetic imaging in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the sub-meter-level detection and identification methods for buried unexploded ordnance based on correlation-based three-dimensional magnetic imaging in the above embodiments.

[0074] The present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned sub-meter level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging.

[0075] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for detecting and identifying sub-meter level buried unexploded ordnance based on correlation three-dimensional magnetic imaging.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A sub-meter-level detection and identification method for buried unexploded ordnance based on correlation-based three-dimensional magnetic imaging, characterized in that, include: Data acquisition steps: Acquire measured data of surface magnetic anomalies and corresponding trajectory data of the detection area, and divide local anomaly areas according to the rate of change of magnetic anomalies; The correlation coefficient calculation steps are as follows: the underground space corresponding to the local anomaly area is divided into a three-dimensional grid, the theoretical magnetic anomaly of each grid node is calculated based on the magnetic dipole model, and the normalized local correlation coefficient is obtained point by point with the measured magnetic anomaly data. Nodes with a value higher than the preset threshold are retained as candidate nodes. Parameter optimization and inversion steps: Using the minimum error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node as the fitness function, the optimal magnetization parameters and the optimal local correlation imaging coefficients are obtained by iterative optimization based on the particle swarm optimization algorithm. Three-dimensional imaging and target recognition steps: Based on the optimal local correlation imaging coefficients, the three-dimensional magnetic imaging results of the detection area are generated by integrating them according to spatial location, and the contour parameters and spatial location of the buried unexploded ordnance at the sub-meter level are obtained.

2. The sub-meter-level detection and identification method for buried unexploded ordnance based on correlation three-dimensional magnetic imaging according to claim 1, characterized in that, The steps for calculating the correlation coefficient include: Based on the local anomaly region, the corresponding underground space is divided into a three-dimensional grid, and each grid node corresponds to a hypothetical uniformly magnetized dipole. Based on the magnetic dipole model, the theoretical magnetic anomaly sequence of each grid node is calculated according to the grid node coordinates, geomagnetic field dip angle, geomagnetic field deflection angle and initial magnetization parameters. Based on the normalized cross-correlation coefficient formula, the local correlation coefficients of the theoretical magnetic anomaly sequence and the measured magnetic anomaly data are calculated point by point to obtain the normalized local correlation coefficient of each node. Set a correlation coefficient threshold and retain grid nodes whose normalized local correlation coefficient is greater than or equal to the threshold as candidate nodes.

3. The sub-meter-level detection and identification method for buried unexploded ordnance based on correlation three-dimensional magnetic imaging according to claim 1, characterized in that, The parameter optimization and inversion steps include: The fitness function is the normalized mean square error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate nodes. The magnetization tilt angle, magnetization deflection angle, and local correlation imaging coefficients are encoded as multidimensional particle positions in the particle swarm algorithm. In each iteration of the particle swarm optimization algorithm, the theoretical magnetic anomaly is recalculated based on the magnetic dipole model, and the particle velocity and position are updated until the fitness function converges or the preset maximum number of iterations is reached. Output the optimal magnetization parameter and optimal local correlation imaging coefficient corresponding to the globally optimal particle position after convergence.

4. The sub-meter-level detection and identification method for buried unexploded ordnance based on correlation three-dimensional magnetic imaging according to claim 2, characterized in that, The three-dimensional imaging and target recognition steps include: Based on the optimal local correlation imaging coefficients, a three-dimensional imaging matrix is ​​obtained by mapping the spatial location to a three-dimensional grid in the detection area. Threshold segmentation is performed on the three-dimensional imaging matrix to obtain the contour parameters and spatial coordinates of the buried unexploded ordnance at the sub-meter level, and the spatial position is output after being correlated with the trajectory data.

5. The method according to claim 1, characterized in that, The data acquisition steps include: Based on airborne magnetic survey equipment, the measured data of surface magnetic anomalies are continuously collected at preset measurement point intervals, and the trajectory data is recorded simultaneously. A regular measurement network corresponding to the measured magnetic anomaly data is formed by interpolation.

6. A sub-meter-level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging, employing the sub-meter-level buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging as described in any one of claims 1-5, characterized in that, include: Data acquisition module: acquires the measured magnetic anomaly data and corresponding trajectory data of the detection area, and divides the local anomaly area according to the rate of change of magnetic anomaly; The correlation coefficient calculation module divides the corresponding underground space into a three-dimensional grid based on the local anomaly area, calculates the theoretical magnetic anomaly of each grid node based on the magnetic dipole model, and obtains the normalized local correlation coefficient point by point with the measured magnetic anomaly data, retaining nodes higher than the preset threshold as candidate nodes. Parameter optimization and inversion module: Using the minimum error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate node as the fitness function, the module iteratively optimizes based on the particle swarm optimization algorithm to obtain the optimal magnetization parameters and the optimal local correlation imaging coefficients. 3D Imaging and Target Recognition Module: Based on the optimal local correlation imaging coefficients, the module integrates them according to spatial location to generate 3D magnetic imaging results of the detection area, and obtains the contour parameters and spatial location of the buried unexploded ordnance at the sub-meter level.

7. The sub-meter-level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging according to claim 6, characterized in that, The correlation coefficient calculation module includes: Based on the local anomaly region, the corresponding underground space is divided into a three-dimensional grid, and each grid node corresponds to a hypothetical uniformly magnetized dipole. Based on the magnetic dipole model, the theoretical magnetic anomaly sequence of each grid node is calculated according to the grid node coordinates, geomagnetic field dip angle, geomagnetic field deflection angle and initial magnetization parameters. Based on the normalized cross-correlation coefficient formula, the correlation coefficient between the theoretical magnetic anomaly sequence and the measured magnetic anomaly data is calculated point by point to obtain the normalized local correlation coefficient of each node. Set a correlation coefficient threshold and retain grid nodes whose normalized local correlation coefficient is greater than or equal to the threshold as candidate nodes.

8. The sub-meter-level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging according to claim 6, characterized in that, The parameter optimization and inversion module includes: The fitness function is the normalized mean square error between the theoretical magnetic anomaly and the measured magnetic anomaly data of the candidate nodes. The magnetization tilt angle, magnetization deflection angle, and local correlation imaging coefficients are encoded as multidimensional particle positions in the particle swarm algorithm. In each iteration of the particle swarm optimization algorithm, the theoretical magnetic anomaly is recalculated based on the magnetic dipole model, and the particle velocity and position are updated until the fitness function converges or the preset maximum number of iterations is reached. Output the optimal magnetization parameter and optimal local correlation imaging coefficient corresponding to the globally optimal particle position after convergence.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the sub-meter-level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the sub-meter-level buried unexploded ordnance detection and identification system based on correlation three-dimensional magnetic imaging as described in any one of claims 1 to 7.

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