An arbitrary dimension magnetic anomaly gradient positioning method for buried target detection
By combining the magnetic dipole model and the particle swarm optimization algorithm, the assumption problem of the inversion method under gradient conditions is solved, and the accurate positioning of the target and cross-medium depth acquisition in arbitrary dimensions are realized, which is suitable for complex detection environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HARBIN ENG UNIV
- Filing Date
- 2024-04-25
- Publication Date
- 2026-05-29
AI Technical Summary
Existing technologies for magnetic anomaly detection make too many assumptions about gradient-condition inversion methods, which makes it impossible to guarantee the validity of these assumptions in real-world complex environments and makes it difficult to accurately locate buried targets under cross-medium conditions.
An arbitrary-dimensional magnetic anomaly gradient localization method based on a magnetic dipole model is adopted. The magnetic anomaly gradient function is solved by particle swarm optimization algorithm, and data fusion is performed by combining information from multiple sensors to obtain the location, magnetic moment modulus and burial depth of the target object.
It enables target localization under arbitrary dimensional gradient sensor configuration, is suitable for complex detection environments, improves positioning accuracy, and acquires target depth information under cross-media conditions.
Smart Images
Figure CN118393576B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of magnetic anomaly detection technology, and more specifically, to an arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets. Background Technology
[0002] In both military and civilian fields, the detection of unexploded ordnance (UXO) has always been a crucial component of security work. Traditional detection methods largely rely on ground operators using portable detection equipment, which is not only inefficient but also exposes operators to potential dangers. To improve detection efficiency and reduce risks, technologies using dynamic platforms such as drones for magnetic anomaly detection have emerged.
[0003] Because ferromagnetic targets have higher magnetic permeability than most media such as air, water, seawater, and soil, their surrounding geomagnetic field is disturbed. Magnetic anomaly detection (MAD) is one method for detecting magnetic disturbances. Specifically, when there is relative movement between the sensor and the observed object, the voltage information output by the sensor contains information about the magnetic field changes of the unknown target's characteristics. This physical phenomenon makes the obtained magnetic anomaly features an important clue for the detection, localization, and identification of hidden targets, especially in cross-media detection scenarios.
[0004] Taking UAV aeromagnetic systems as an example, the process of performing magnetic detection generally involves planning the sensitive area, then scanning along comb-shaped survey lines to obtain information about the work area, and then obtaining a magnetic map through interpolation fitting methods. Subsequently, the planar position of the target is often determined manually, which is not rigorous and has a high error rate. Therefore, more and more research is trending towards the analysis of magnetic anomaly signals to invert the precise spatial position of the target. A typical method is to solve the nonlinear equation problem constructed by the magnetic dipole itself, and then use corresponding optimization algorithms, such as the Levenberg-Marquardt (LM) algorithm, Particle Swarm Optimization (PSO) algorithm, etc., to solve the problem. However, this method does not analyze the applicability conditions of its solution, and only provides an analysis under a single magnetometer array element. In the face of complex environmental scenarios with large time variations and high gradients, gradient magnetometers are a commonly used method. Current research has added too many assumptions to the inversion method under gradient conditions, such as setting the direction of the gradient system to be vertical and having a single dimension; making certain assumptions about magnetic moment information or geomagnetic field direction information in the inverted parameters, etc. In the actual complex detection environment, it may not be possible to guarantee the validity of the above assumptions.
[0005] Furthermore, in the detection of unexploded ordnance, which often involves cross-medium issues, it is necessary to further determine the target's burial depth or underwater depth. However, relying solely on magnetic sensor information combined with positioning information is insufficient for further analysis of the retrieved data. In such cases, it is necessary to configure lidar, barometric altimeters, and other sensors to further fuse the information and obtain precise target information.
[0006] Therefore, there is an urgent need to develop an arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets that overcomes the above-mentioned shortcomings. Summary of the Invention
[0007] This invention provides an arbitrary-dimensional magnetic anomaly gradient localization method and system for buried target detection. It addresses the problem that current research often involves excessive assumptions in gradient-condition inversion methods, such as setting the gradient system to a single directional dimension and making certain assumptions about magnetic moment or geomagnetic field direction information in the inverted parameters. These assumptions may not hold true in complex real-world detection environments. By adopting this arbitrary-dimensional magnetic anomaly gradient localization method and system for buried target detection, target localization can be achieved under arbitrary-dimensional gradient sensor configurations. Furthermore, it does not impose excessive constraints on target solving, making it more widely applicable and suitable for complex real-world detection environments.
[0008] To achieve the above objectives, the present invention provides an arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets, comprising:
[0009] Optimization function modeling steps: Construct an arbitrary-dimensional magnetic anomaly gradient function based on the magnetic dipole model;
[0010] Optimized solution steps: Based on the arbitrary-dimensional magnetic anomaly gradient function, the position and magnetic moment magnitude of the target object are obtained through particle swarm optimization algorithm;
[0011] Data fusion step: Based on the location and magnetic moment modulus of the target object, information from multiple sensors is fused to obtain the burial depth of the target object under cross-medium conditions.
[0012] Furthermore, the gradient function of the arbitrary-dimensional magnetic anomaly is:
[0013]
[0014] Furthermore, the optimization function modeling step includes:
[0015] Step 1: Construct a magnetic dipole model. The induced magnetic field B generated by the dipole is represented as:
[0016]
[0017] Where μ0 is the free magnetic permeability, r = [xyz]T Let m be the coordinate vector of the magnetic dipole relative to the sensor, and m = [m x m y m z ] T It is the magnetic moment vector;
[0018] Step 2: Based on the magnetic dipole model, the scalar magnetic field information at the nth observation point is represented as:
[0019]
[0020] Among them, s t Let s be the position coordinates of the target object in the spatial coordinate system. n Let G be the sensor location coordinates at the nth observation point, and G be the geomagnetic field modulus information. Let θ be the direction vector of the Earth's magnetic field, where θ is the geomagnetic declination. It is the geomagnetic tilt angle. The observed scalar field at the nth observation point;
[0021] The scalar magnetic field information expression for the nth observation point can be simplified to:
[0022]
[0023] Step 3: Based on the scalar magnetic field information expression at the nth measurement point and the observation information from multiple observation points, a set of nonlinear equations concerning the scalar magnetic field information expression is constructed as follows:
[0024]
[0025] Step 4: Based on the nonlinear equations relating to the scalar magnetic field information, assuming the baseline distance between sensor 1 and sensor 2 under arbitrary dimensional gradients is d, the following equations for arbitrary dimensional magnetic anomaly gradients are constructed:
[0026]
[0027] The optimization function of the arbitrary-dimensional magnetic anomaly gradient equation system can be expressed as:
[0028]
[0029] in, This is an estimate of the direction of the Earth's magnetic field. This is the estimated position of the target object. This is the estimated magnetic moment of the target object. The position of the i-th observation point of sensor 1 The position of the i-th observation point of sensor 2 This refers to the differential magnetic field information of sensor 1 and sensor 2 at the i-th point.
[0030] Furthermore, the optimization function modeling step also includes:
[0031] Obtain the geomagnetic declination θ and geomagnetic dip angle of the area where the target object is located. To optimize the solution space, the following constraints are added to the set of equations for the gradient of magnetic anomalies in any dimension:
[0032]
[0033] Where θ wmm , This refers to the geomagnetic declination and inclination of the region where the target object is located, obtained by querying the WMM geomagnetic model.
[0034] Furthermore, the optimization solution steps include:
[0035] The arbitrary-dimensional magnetic anomaly gradient function is used as the fitness function of the particle swarm optimization algorithm to solve for the position and magnetic moment magnitude of the target object.
[0036] The velocity update formula and position update formula for the i-th particle in the j-th spatial dimension during the (k+1)-th iteration are expressed as follows:
[0037] v ij (k+1)=wv ij (k)+η1r1(p ij -s ij (k))+η2r2(p gj -s ij (k))
[0038] s ij (k+1)=s ij (k)+v ij (k+1)
[0039] Among them, v ij Let s be the velocity vector of the i-th particle in dimension j. ij Let p be the position vector of the i-th particle in dimension j. ij Let p be the current optimal position of the i-th particle in dimension j. gj For the globally optimal position in dimension j, s ij Let w be the current position of the i-th particle in dimension j, w be the inertia factor, η1 and η2 be the learning factors, and r1 and r2 be random numbers between [0,1].
[0040] Furthermore, the optimization solution steps include:
[0041] The iteration continues until the loop termination condition is met, which is reaching the maximum iteration time, reaching the maximum number of iterations, or finding the optimal solution of the fitness function.
[0042] Furthermore, the data fusion step includes:
[0043] Based on the location of the target object and the information from the UAV and LiDAR sensors, the altitude of the UAV platform and the altitude of the LiDAR at the target object's location are determined through spatial interpolation. Therefore, the burial depth of the target object is:
[0044] h t =|z s +z t |-h l
[0045] Among them, h t z is the burial depth of the target object. s Z represents the altitude of the unmanned aerial vehicle platform. t h represents the altitude of the target object. l The height of the lidar.
[0046] Furthermore, the present invention provides an arbitrary-dimensional magnetic anomaly gradient localization system for buried target detection, which applies the above-mentioned arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection, including:
[0047] Optimization function modeling module: used to construct magnetic anomaly gradient functions of arbitrary dimensions based on the magnetic dipole model;
[0048] Optimization and solution module: used to obtain the position and magnetic moment magnitude of the target object based on the arbitrary-dimensional magnetic anomaly gradient function using a particle swarm optimization algorithm;
[0049] Data fusion module: Based on the location and magnetic moment modulus of the target object, it fuses information from multiple sensors to obtain the burial depth of the target object under cross-medium conditions.
[0050] 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-described arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets.
[0051] 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 arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets.
[0052] Compared with existing technologies, the advantages and positive effects of this invention are as follows: Based on the magnetic dipole model, this invention constructs a nonlinear equation for target position inversion under multiple measurement points by extending the direction of the gradient magnetometer system to arbitrary dimensions. A particle swarm optimization algorithm is then used to solve this equation. This addresses the problem of current research adding too many assumptions to gradient-condition inversion methods, such as assuming a single directional dimension of the gradient system or making certain assumptions about magnetic moment or geomagnetic field direction information in the inverted parameters. These assumptions may not hold true in complex real-world detection environments. This invention effectively suppresses environmental noise, enabling target localization under arbitrary-dimensional gradient sensor configurations. Furthermore, it avoids adding excessive constraints, broadening its application scenarios, reducing the adverse effects of assumptions, and improving the localization accuracy of magnetic targets. In addition, this invention integrates information from multiple sensors to further analyze the inversion information, enabling the acquisition of the target's burial depth under cross-medium conditions. This makes it suitable for target detection under cross-medium conditions, such as the detection of unexploded ordnance or underwater depth. Attached Figure Description
[0053] Figure 1 This is a flowchart of the arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets according to the present invention;
[0054] Figure 2 This is a schematic diagram of the aeromagnetic observation of the UAV according to the present invention;
[0055] Figure 3 This is a schematic diagram of the gradient in any dimension of the present invention;
[0056] Figure 4 This is a schematic diagram of the target object depth calculation in this invention;
[0057] Figure 5 This is a schematic diagram of a computer device provided in an embodiment of the present invention.
[0058] In the above image:
[0059] 40. via bus; 41. processor; 42. memory; 43. communication interface. Detailed Implementation
[0060] 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 skilled in the art without creative effort are within the scope of protection of this application.
[0061] 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.
[0062] 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.
[0063] like Figures 1-5 As shown, this invention provides an arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets.
[0064] Example 1:
[0065] Figure 1 This is a flowchart of the arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection according to the present invention. The following is in conjunction with... Figure 1 Please provide a detailed explanation of this active guidance control method.
[0066] Optimization function modeling step S100: Construct an arbitrary-dimensional magnetic anomaly gradient function based on the magnetic dipole model, specifically including the following steps:
[0067] Step 1: Construct a magnetic dipole model. In magnetic anomaly detection, when the closest path approach (CPA) between the target object (such as unexploded ordnance or buried metal objects) and the detection sensor is much larger than the maximum linear scale of the target object, a magnetic dipole model can be used to approximate the magnetic field characteristics of the target object. Therefore, by constructing a magnetic dipole model to analyze the induced magnetic anomaly signal, the induced magnetic field B generated by the dipole is represented as:
[0068]
[0069] Where μ0 is the free magnetic permeability, r = [xyz] T Let m be the coordinate vector of the magnetic dipole relative to the sensor, and m = [m x m y m z ] T It is the magnetic moment vector;
[0070] Step 2: As Figure 2 As shown, in UAV aeromagnetic surveys, optically pumped magnetometers are generally used as the primary detection sensor. During the observation process, the UAV flies along a planned route, collecting magnetic field data at multiple locations using the optically pumped magnetometer. Based on the magnetic dipole model, the scalar magnetic field information at the nth measurement point can be described using the following formula:
[0071]
[0072] Among them, s t Let s be the position coordinates of the target object in the spatial coordinate system. n Let G be the sensor location coordinates at the nth observation point, and G be the geomagnetic field modulus information. Let θ be the direction vector of the Earth's magnetic field, where θ is the geomagnetic declination. It is the geomagnetic tilt angle. The observed scalar field at the nth observation point;
[0073] The scalar magnetic field information expression for the nth observation point can be simplified to:
[0074]
[0075] Step 3: Based on the scalar magnetic field information expression at the nth measurement point and the observation information from multiple observation points, a set of nonlinear equations concerning the scalar magnetic field information expression is constructed as follows. This parameter inversion problem can be transformed into a least-squares problem of a nonlinear equation as shown in the following equation:
[0076]
[0077] Based on the aforementioned nonlinear equations relating to the scalar magnetic field information, a common assumption is that the short-term magnetic background field is constant. This can be reduced to eight parameters by estimating the background geomagnetic field amplitude and removing the DC component. However, in reality, the geomagnetic field is influenced by factors such as topography and geological structures, leading to the existence of geomagnetic gradients, thus rendering this assumption invalid.
[0078] like Figure 3As shown, to address the aforementioned issues, gradient magnetometers are typically used in near-Earth unexploded ordnance detection to observe the geomagnetic field and utilize the acquired gradient information to eliminate interference. Gradient magnetometers can measure the gradient of the geomagnetic field in different directions, including the x, y, and z directions relative to the carrier. Different gradient design methods correspond to different data calculation methods to adapt to different detection needs and environments.
[0079] This invention extends the application of gradient information to arbitrary dimensions to adapt to more complex detection scenarios. The parameter inversion process under arbitrary-dimensional gradient information conditions is detailed below:
[0080] Step 4: Based on the nonlinear equations relating to the scalar magnetic field information expression, assuming the baseline distance between sensor 1 and sensor 2 under arbitrary dimensional gradients is d, the following equations for arbitrary dimensional magnetic anomaly gradients are constructed:
[0081]
[0082] Based on the above set of equations for magnetic anomaly gradients in any dimension, the optimization function of the set of equations for magnetic anomaly gradients in any dimension can be expressed as:
[0083]
[0084] in, This is an estimate of the direction of the Earth's magnetic field. The estimated position of the target object. This is the estimated magnetic moment of the target object. Let i be the position of the i-th observation point of sensor 1. The position of the i-th observation point of sensor 2 This represents the differential magnetic field information between sensor 1 and sensor 2 at the i-th point.
[0085] The direction of the background geomagnetic field in the detection area has certain prior information, and the geomagnetic declination θ and geomagnetic dip angle of the target area can be obtained by querying a geomagnetic model. Assuming a small discrepancy exists between the actual geomagnetic field direction and the query model, appropriate constraints can be added to the problem. The geomagnetic declination θ and geomagnetic dip angle of the target area are then obtained. To optimize the solution space, the following constraints are added to the system of equations for magnetic anomaly gradients in any dimension:
[0086]
[0087] Where θ wmm , To obtain the geomagnetic declination and inclination of the target area by querying the WMM geomagnetic model, it is assumed that there is a 1° deviation between the actual geomagnetic angle and the theoretical geomagnetic angle.
[0088] Under the condition of gradient information, the geomagnetic field quantity is canceled out, and the optimization function of the above arbitrary-dimensional magnetic anomaly gradient equation set also has 8 parameters. Through the above steps, the optimization function modeling process of the target location algorithm under the airborne magnetic detection system is completed.
[0089] Optimization solution step S200: Based on the arbitrary-dimensional magnetic anomaly gradient function, the position and magnetic moment magnitude of the target object are obtained through particle swarm optimization algorithm.
[0090] In some embodiments, the particle swarm optimization (PSO) algorithm is used to solve the optimization problem of the optimization function of the above-mentioned arbitrary-dimensional magnetic anomaly gradient equation system. First, it is necessary to complete the mapping from magnetic survey data to geographic information data, and then use a coordinate transformation system to transform the data in the geographic coordinate system to the spatial rectangular coordinate system.
[0091] Particle swarm optimization (PSO) is a swarm intelligence-based search algorithm that searches for optimal solutions by simulating the social behavior of flocks of birds or schools of fish. PSO uses multiple particles carrying properties related to the solution parameters for optimization. Based on the problem model discussed above, the operational steps of the PSO optimization algorithm are as follows:
[0092] During initialization, it is assumed that there are n particles S = {S1, S2, ... S} in the space. n In this problem, the space occupied by each particle is 9-dimensional, and the spatial position of one particle is the corresponding solution quantity. Furthermore, all particles have a corresponding velocity attribute v in each spatial dimension. i It is used for position movement in each particle search step.
[0093] Combining the sensor's position information and its observation data, the fitness of each particle is calculated according to the following formula, and the particle's position and velocity attributes in space are updated. The update is based on the optimal solution p found by the particle itself. i And the global optimal solution p of the population g Then, the velocity update and position of the i-th particle in the j-th spatial dimension during the (k+1)-th iteration satisfy the following equation:
[0094] v ij (k+1)=wv ij (k)+η1r1(p ij -s ij (k))+η2r2(p gj -s ij (k))
[0095] s ij (k+1)=s ij (k)+v ij (k+1)
[0096] Among them, v ij Let s be the velocity vector of the i-th particle in dimension j. ij Let p be the position vector of the i-th particle in dimension j. ij Let p be the current optimal position of the i-th particle in dimension j. gj For the globally optimal position in dimension j, s ij Let w be the current position of the i-th particle in dimension j, w be the inertia factor, η1 and η2 be the learning factors, and r1 and r2 be random numbers between [0,1].
[0097] In some embodiments, w is an inertia factor that balances global and local search. This invention uses a dynamically adjusted w value. As the number of iterations increases, the w value decreases from 0.9 to 0.4, making the search range of the algorithm approach local search from the initial global search. η1 and η2 are learning factors used to adjust the speed at which the particle moves towards its own optimal position and the global optimal position. The value of 1.5 recommended in relevant literature is used.
[0098] The algorithm searches for the optimal solution through multiple iterations. After each iteration, it checks whether a termination condition is met, such as whether the maximum number of iterations has been reached, whether the time limit has been reached, or whether the global optimal solution remains consistent. If the termination condition is met, the algorithm terminates; otherwise, it continues iterating.
[0099] Through the above steps, the optimized solution for the magnetic anomaly gradient localization method for buried targets was completed. This method is not only applicable to single-dimensional gradient information, but can also handle complex detection scenarios in arbitrary dimensions, improving the accuracy and reliability of target localization.
[0100] In real-world detection scenarios, the target and the detector often exist in a cross-medium manner, such as buried unexploded ordnance underground or underwater vehicles. This requires corresponding auxiliary sensors to provide the location and depth of the target in the medium based on the above-mentioned solutions.
[0101] like Figure 4 As shown, in some embodiments, a data fusion step S300 is further included: based on the location and magnetic moment modulus of the target object, information from multiple sensors is fused to obtain the burial depth of the target object under cross-medium conditions. Taking the underground buried object scenario as an example, the data fusion step S300 is explained in detail.
[0102] During the flight path planning test of the dynamic platform, its flight altitude in the navigation coordinate system can be considered constant within a relatively small area. Therefore, a highly accurate fitting map of the UAV's altitude and the lidar sensor's ranging altitude information can be obtained through spatial interpolation. If the actual location of the buried object in the navigation coordinate system is known, the UAV's altitude and the lidar's set altitude at that location can be determined, and the burial depth can then be calculated using the following formula:
[0103] h t =|z s +z t |-h l
[0104] Among them, h t z is the burial depth of the target object. s Z represents the altitude of the unmanned aerial vehicle platform. t h represents the altitude of the target object. l The height of the lidar.
[0105] Through the above solution process, information including the target's location, depth under cross-medium conditions, and the target's magnetic moment can be obtained. This allows for the determination of the target's burial depth under cross-medium conditions, and is applicable to target detection under cross-medium conditions, such as the detection of unexploded ordnance or underwater depth.
[0106] Example 2:
[0107] This invention provides an arbitrary-dimensional magnetic anomaly gradient localization system for buried target detection, which, when applied to the aforementioned arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection, includes:
[0108] Optimization function modeling module: used to construct magnetic anomaly gradient functions of arbitrary dimensions based on the magnetic dipole model;
[0109] Optimization and solution module: used to obtain the position and magnetic moment magnitude of the target object based on the magnetic anomaly gradient function of arbitrary dimensions using the particle swarm optimization algorithm;
[0110] Data fusion module: Based on the location and magnetic moment modulus of the target object, it fuses information from multiple sensors to obtain the burial depth of the target object under cross-medium conditions.
[0111] Example 3:
[0112] Combination Figure 5 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.
[0113] 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.
[0114] 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.
[0115] 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.
[0116] 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.
[0117] The processor 41 reads and executes the computer program instructions stored in the memory 42 to implement the arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection in the above embodiment.
[0118] In some embodiments, the computer device may further include a communication interface 43 and a bus 40. For example, Figure 4 As shown, the processor 41, memory 42, and communication interface 43 are connected through bus 40 and complete communication with each other.
[0119] Communication interface 43 is used to enable communication between modules, devices, units and / or equipment in the embodiments of this application.
[0120] 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.
[0121] 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).
[0122] 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.
[0123] Furthermore, in conjunction with the arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection described 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 arbitrary-dimensional magnetic anomaly gradient localization methods for buried target detection described in the above embodiments.
[0124] 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 arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets.
[0125] 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 arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets.
[0126] 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 method for arbitrary-dimensional magnetic anomaly gradient localization for buried target detection, characterized in that, include: Optimization function modeling steps: Construct an arbitrary-dimensional magnetic anomaly gradient function based on the magnetic dipole model; Optimized solution steps: Based on the arbitrary-dimensional magnetic anomaly gradient function, the position and magnetic moment magnitude of the target object are obtained through particle swarm optimization algorithm; Data fusion step: Based on the location and magnetic moment modulus of the target object, information from multiple sensors is fused to obtain the burial depth of the target object under cross-medium conditions; The optimization function modeling step includes: Step 1: Construct a magnetic dipole model. The induced magnetic field B generated by the dipole is represented as: in, The permeability of free space, Let be the coordinate vector of the magnetic dipole relative to the sensor. It is the magnetic moment vector; Step 2: Based on the magnetic dipole model, the scalar magnetic field information at the nth observation point is represented as: in, These are the position coordinates of the target object in a spatial coordinate system. Let n be the sensor position coordinates at the nth observation point. G Information on the magnitude of the geomagnetic field. Let be the direction vector of the Earth's magnetic field, where It is the magnetic declination. It is the geomagnetic tilt angle. For the first n The observed scalar field at each observation point; The scalar magnetic field information expression for the nth observation point can be simplified to: Step 3: According to the above... n The scalar magnetic field information expression at each measuring point and the observation information from multiple observation points are used to construct a set of nonlinear equations concerning the scalar magnetic field information expression: Step 4: Based on the nonlinear equations relating to the scalar magnetic field information expression, assuming that the baseline distance between sensor 1 and sensor 2 under arbitrary dimensional gradients is... d The system of equations for the gradient of magnetic anomalies of arbitrary dimensions is constructed as follows: The optimization function of the arbitrary-dimensional magnetic anomaly gradient equation system can be expressed as: in, This is an estimate of the direction of the Earth's magnetic field. This is the estimated position of the target object. This is the estimated magnetic moment of the target object. For the first sensor 1 i The location of each observation point For the sensor 2's first i The location of each observation point For the sensor 1 and the sensor 2 in the first i Differential magnetic field information at each point; Obtain the geomagnetic declination of the area where the target object is located. With geomagnetic tilt To optimize the solution space, corresponding constraints are added to the arbitrary-dimensional magnetic anomaly gradient equation system: in This refers to the geomagnetic declination and inclination of the region where the target object is located, obtained by querying the WMM geomagnetic model.
2. The arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets according to claim 1, characterized in that, The optimization solution steps include: The arbitrary-dimensional magnetic anomaly gradient function is used as the fitness function of the particle swarm optimization algorithm. The position and magnetic moment magnitude of the target object are determined. No. i The particle in the first k+ In the 1st iteration j The velocity update formula and position update formula in each spatial dimension are expressed as follows: in, For the first i Individual particles j The velocity vector of a particle in a dimension For the first i Individual particles j Position vector in dimension For the first i Individual particles j The current optimal position in the dimension, In order to be in j The globally optimal position in the dimension. For the first i Individual particles j The current position in the dimension, where w is the inertia factor. and As a learning factor, and It is a random number between [0, 1].
3. The arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection according to claim 2, characterized in that, The optimization solution steps include: The iteration continues until the loop termination condition is met, which is reaching the maximum iteration time, reaching the maximum number of iterations, or finding the optimal solution of the fitness function.
4. The arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection according to claim 1, characterized in that, The data fusion steps include: Based on the location of the target object and the information from the UAV and LiDAR sensors, the altitude of the UAV platform and the altitude of the LiDAR at the target object's location are determined through spatial interpolation. Therefore, the burial depth of the target object is: in, The burial depth of the target object. The altitude of the drone platform. The altitude of the target object. The height of the lidar.
5. A magnetic anomaly gradient localization system for detecting buried targets in any dimension, characterized in that, The arbitrary-dimensional magnetic anomaly gradient localization method for detecting buried targets according to any one of claims 1-4 includes: Optimization function modeling module: used to construct magnetic anomaly gradient functions of arbitrary dimensions based on the magnetic dipole model; Optimization and solution module: used to obtain the position and magnetic moment magnitude of the target object based on the arbitrary-dimensional magnetic anomaly gradient function using a particle swarm optimization algorithm; Data fusion module: used to fuse information from multiple sensors based on the location and magnetic moment modulus of the target object to obtain the burial depth of the target object under cross-medium conditions; The optimization solution module includes: Construct a magnetic dipole model, and represent the induced magnetic field B generated by the dipole as: in, The permeability of free space, Let be the coordinate vector of the magnetic dipole relative to the sensor. It is the magnetic moment vector; Based on the magnetic dipole model, the scalar magnetic field information at the nth observation point is represented as: in, These are the position coordinates of the target object in a spatial coordinate system. Let n be the sensor position coordinates at the nth observation point. G Information on the magnitude of the geomagnetic field. Let be the direction vector of the Earth's magnetic field, where It is the magnetic declination. It is the geomagnetic tilt angle. For the first n The observed scalar field at each observation point; The scalar magnetic field information expression for the nth observation point can be simplified to: According to the first n The scalar magnetic field information expression at each measuring point and the observation information from multiple observation points are used to construct a set of nonlinear equations concerning the scalar magnetic field information expression: Based on the aforementioned set of nonlinear equations relating to the scalar magnetic field information, assuming that the baseline distance between sensor 1 and sensor 2 under any dimensional gradient is... d The system of equations for the gradient of magnetic anomalies of arbitrary dimensions is constructed as follows: The optimization function of the arbitrary-dimensional magnetic anomaly gradient equation system can be expressed as: in, This is an estimate of the direction of the Earth's magnetic field. This is the estimated position of the target object. This is the estimated magnetic moment of the target object. For the first sensor 1 i The location of each observation point For the sensor 2's first i The location of each observation point For the sensor 1 and the sensor 2 in the first i Differential magnetic field information at each point; Obtain the geomagnetic declination of the area where the target object is located. With geomagnetic tilt To optimize the solution space, corresponding constraints are added to the arbitrary-dimensional magnetic anomaly gradient equation system: in This refers to the geomagnetic declination and inclination of the region where the target object is located, obtained by querying the WMM geomagnetic model.
6. 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 arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection as described in any one of claims 1 to 4.
7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the arbitrary-dimensional magnetic anomaly gradient localization method for buried target detection as described in any one of claims 1 to 4.