Short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio, measurement array and system

Abstract

The invention discloses a short baseline constraint optimization positioning method based on a magnetic moment vector modulus ratio, a measurement array and a system, and belongs to the technical field of magnetic detection and positioning. The method comprises the following steps: firstly, constructing a regular triangular prism-shaped measurement array, collecting magnetic field vector data generated by a magnetic target at each vertex measurement point of the array, and calculating a magnetic field vector at a coordinate origin of the measurement array and a vector mode gradient of the magnetic field vector; the spatial position coordinates of the magnetic target are used as decision variables, a constraint optimization model used for magnetic dipole positioning solution is established, and the target function of the constraint optimization model is a magnetic moment vector modulus ratio function; solving the constraint optimization model by adopting an adaptive differential evolution dung beetle optimization algorithm to obtain an optimal spatial position coordinate of the magnetic target; and based on the optimal spatial position coordinates, performing inversion calculation on three components of the magnetic moment vector of the magnetic target. According to the invention, the space volume of the positioning system is reduced, the positioning system is relatively simple, and the positioning error caused by the uncertainty of magnetic conductivity is also eliminated.

Description

Technical Field

[0001] This invention belongs to the field of magnetic detection and positioning technology, specifically relating to a short baseline constraint optimization positioning method, measurement array and system based on the magnetic moment vector modulus ratio. Background Technology

[0002] Magnetic anomaly location technology, originating from geomagnetic exploration, determines the location, orientation, or other attributes of magnetic targets by detecting and analyzing local distortions in the geomagnetic field (i.e., magnetic anomalies). It is a passive location method with excellent concealment. Today, this technology has been extended to many important fields such as underwater exploration, medical device tracking, indoor navigation, and industrial inspection.

[0003] The physical model-based positioning method utilizes the magnetic field model of the magnetic target. It only requires measuring the magnetic field at a few points in the space around the magnetic target to inversely deduce the position and orientation of the magnetic target. Therefore, it has received widespread attention and in-depth research.

[0004] Blankenbach et al. proposed an indoor positioning system based on an artificial quasi-static magnetic field, in which the reference point is composed of coils to generate a periodic static magnetic field. The moving point is equipped with a magnetic sensor, which determines the three-dimensional position by measuring the field strength of at least three reference coils (Blankenbach J, Norrdine A. Building informationsystems based on precise indoor positioning[J]. Journal of Location-BasedServices, 2011, 5(1): 22-37.). Hu Chao et al. discussed a method for locating magnets in capsules based on a magnetic field sensor array. Through algorithm and system optimization design, they achieved real-time three-dimensional positioning and tracking based on a magnetic dipole model (Hu Chao, Song Shuang, Yang Wan'an, et al. Research on magnetic field positioning technology for capsule endoscope position orientation[J]. Integrated Technology, 2012, 1(1): 105-113.). Based on this, they proposed a wearable magnetic positioning model by increasing the number of magnetic sensors, and combined the least squares algorithm and particle swarm optimization algorithm to optimize the positioning inverse algorithm, thus achieving good results in terms of efficiency and accuracy (Wang Wenhu. Capsule endoscope tracking and positioning based on embedded system [D]. Master's thesis of Ningbo University, 2014.).

[0005] Hu et al. proposed a linear positioning method based on the orthogonality between the magnetic moment, magnetic flux density vector, and dipole-sensor orientation vector (Hu C, Meng MQH, Mandal M. A linear algorithm for tracing magnet position and orientation by using three-axis magnetic sensors[J]. IEEE Transactions on Magnetics, 2007, 43(12): 4096-4101.). Nara et al. utilized the magnetic field of the magnetic dipole and Euler's equations, and by introducing the surface integral property of magnetic flux density, directly correlated the integral constraint of the magnetic field with the position of the magnetic dipole, deriving a linear formula based on the surface integral of magnetic flux density. These linear methods provide closed-form solutions to Euler's equations with low computational cost; however, the positioning systems are complex and the sources of system errors are diverse (Nara T, Watanabe H, Ito W. Properties of the linear equations derived from euler's sequence and its application to magnetic dipole localization[J]. IEEE Transactions on Magnetics, 2012, 48(11): 4444-4447.). Yang et al. proposed a six-dimensional magnetic localization method based on particle swarm optimization algorithm to determine the three-dimensional position and three-dimensional orientation of a rectangular magnet (Yang W, Hu C, Meng MQH, et al. A six-dimensional magnetic localization algorithm for arectangular magnet objective based on a particle swarm optimizer[J]. IEEE Transactions on Magnetics, 2009, 45(8): 3092-3099.).Lv et al. proposed a Levberg-Marquardt localization method based on error fusion, aiming to achieve robust magnetic tracking under rapidly decaying magnetic field interference environments (Lv B, Dai H, Qin Y, et al. A High-Precision Magnetic Localization Method Based on EFLM for Attenuating Interference[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 1-10.). The decision variables for these nonlinear localization methods typically include three-dimensional spatial coordinates and three-dimensional magnetic moment components, which requires more measurement data or other prior information.

[0006] This invention utilizes the invariance of the magnetic moment vector magnitude and the characteristics of the magnetic dipole magnetic field to propose a short-baseline constraint-based optimization positioning method. Its decision variables are only the three-dimensional spatial coordinates, thus allowing for the positioning of magnetic targets without knowing the magnetic moment, using a relatively small number of triaxial magnetometers and a two-layer symmetrical triangular prism measurement array. The measurement array also has the advantages of integration and a short baseline, reducing the spatial volume of the positioning system and simplifying the system. It also eliminates positioning errors caused by uncertainties in magnetic permeability. The magnetic moment vector is directly calculated using the magnetic field vector measurements at different measurement points and the positioning results, offering the advantage of high computational speed. Summary of the Invention

[0007] The purpose of this invention is to provide a short baseline constraint optimization positioning method, measurement array, and system based on the magnetic moment vector modulus ratio.

[0008] The objective of this invention is achieved through the following technical solution:

[0009] A short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio includes the following steps:

[0010] A regular triangular prism measurement array is constructed, and magnetic field vector data generated by the magnetic target at each vertex measurement point of the array are collected. The magnetic field vector and its vector magnitude gradient at the origin of the measurement array coordinates are calculated.

[0011] Using the spatial coordinates of the magnetic target as the decision variable, a constrained optimization model is established for solving the magnetic dipole localization problem. The objective function is the magnetic moment vector modulus ratio function.

[0012] The optimal spatial coordinates of the magnetic target are obtained by solving the constrained optimization model using the adaptive differential evolution dung beetle optimization algorithm.

[0013] Based on the optimal spatial coordinates, the three components of the magnetic moment vector of the magnetic target are calculated by inversion.

[0014] Furthermore, the regular triangular prism measuring array includes six triaxial magnetometers, each located at one of the six vertices of the regular triangular prism, i.e., each vertex corresponds to a magnetic measurement point; in the regular triangular prism measuring array, the two bases are equilateral triangles with circumradius of [missing information]. The height of the regular triangular prism measuring array is The origin of the spatial rectangular coordinate system is the midpoint of the line connecting the circumcenters of the two base surfaces. The coordinate system has its y-axis parallel to one side of the bottom surface and points from one end of that side to the other, and its z-axis parallel to the edge and points from the lower bottom surface to the upper bottom surface.

[0015] Furthermore, the origin of the calculation coordinates magnetic field vector at the location and its vector magnitude gradient , and ,

[0016]

[0017]

[0018] in, , , , , and This represents the measured value of the magnetic field vector. , , , , and This corresponds to the magnetic field vector magnitude; , , , , and The positions of the six triaxial magnetometers are indicated; B(O) represents the magnetic field vector magnitude at point O.

[0019] Furthermore, using the spatial coordinates of the magnetic target For the decision variables, establish the following constrained optimization model: Objective function for:

[0020]

[0021] in, The number of measurement points; , Indicates by the first The magnetic dipole magnetic moment vector magnitude calculated from the data of each measuring point Each vertex of the regular triangular prism measurement array corresponds to a measurement point. ; Indicates the first Each measuring point relative to the magnetic target position vector; constant , ρ is the magnetic permeability of the medium; Indicates the first Individual measuring points and magnetic targets The distance between them; For magnetic targets in the first The magnetic field vector at each measuring point This corresponds to the magnetic field vector magnitude;

[0022] The constraints are:

[0023]

[0024] in, , Indicates magnetic target The distance between the measurement array coordinate origin O and the coordinate system origin O; express Spatial gradient.

[0025] Furthermore, the adaptive differential evolution dung beetle optimization algorithm includes:

[0026] (1) The population is initialized using a hybrid strategy of Chebyshev chaotic mapping and reverse learning;

[0027] (2) The individuals in the initial population are divided into four categories: rolling crickets, breeding crickets, thieving crickets and foraging crickets, and their positions are pre-updated respectively;

[0028] (3) Perform differential evolution mutation and crossover operations on the pre-updated population;

[0029] (4) The global optimal solution is updated using a greedy selection strategy, and the mutation factor and crossover probability parameters are updated adaptively using the Lymer mean and arithmetic mean;

[0030] (5) Repeat steps (2) to (4) until the preset maximum number of iterations is reached, and output the global optimal solution as the spatial coordinates of the magnetic target. .

[0031] Furthermore, the spatial position coordinates of the magnetic target based on the optimal solution Inversion calculation of the three components of the magnetic moment vector of the magnetic target , and ,

[0032]

[0033]

[0034]

[0035] in, , and They represent the first The three components of the magnetic moment vector obtained from the magnetic field values ​​at each measuring point.

[0036] Furthermore, the aforementioned , and Specifically, it is expressed as follows:

[0037]

[0038]

[0039]

[0040] in, , , , .

[0041] A measurement array for a short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio, wherein the measurement array is a regular triangular prism array composed of six triaxial magnetometers; the bases of the upper and lower parallel equilateral triangles are... and Six triaxial magnetometers are located at the six vertices of the regular triangular prism. , , , , and At each location, numbered sequentially from 1 to 6, the sensing axes of the six triaxial magnetometers are aligned with each other; the circumradius of the bases of the two equilateral triangles is defined as... ; located in outer core and The midpoint of the line connecting the circumcenters is the origin O of the spatial rectangular coordinate system. shaft and The sides are parallel and by point to , shaft and The sides are parallel and by point to , The length of the side is , that is, the height of the regular triangular prism measuring array;

[0042] The six vertices correspond to six magnetic measurement points, and the spatial coordinates of each measurement point are as follows: , , , , , .

[0043] A computer device system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio.

[0044] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a short baseline constraint optimization positioning method based on the magnetic moment vector modulus ratio.

[0045] The beneficial effects of this invention are as follows:

[0046] (1) In the method of locating a magnetic dipole using the measured value of the magnetic field or its gradient, the unknowns usually include three-dimensional spatial position coordinates and three-dimensional magnetic moment components. This invention utilizes the invariance of the magnetic moment vector magnitude and the characteristics of the magnetic dipole magnetic field to propose a short baseline constraint optimization positioning method based on the magnetic moment vector magnitude ratio. Its unknowns are only three-dimensional spatial position coordinates. Therefore, a relatively small number of triaxial magnetometers and a two-layer symmetrical structure of regular triangular prism measurement array can be used. It also has the advantages of integration and short array baseline, which reduces the volume of the measurement array.

[0047] (2) The short baseline constraint optimization positioning algorithm for solving the position coordinates of magnetic dipole proposed in this invention does not require prior knowledge of the magnetic moment of the magnetic dipole, and also eliminates the positioning error caused by the uncertainty of magnetic permeability.

[0048] (3) The three components of the magnetic moment are directly calculated and statistically averaged from the magnetic field vector measurement values ​​and magnetic dipole positioning results at different measurement points. This method has the advantages of fast calculation speed and noise suppression. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a triaxial magnetometer array of regular triangular prisms;

[0050] Figure 2 This is a flowchart of a short baseline constraint optimization positioning algorithm based on magnetic moment vector modulus ratio.

[0051] Figure 3It is a curve showing the absolute error of the magnetic dipole positioning solution as a function of the standard deviation of the triaxial magnetometer noise.

[0052] Figure 4 It is a curve showing the absolute error value of the magnetic dipole magnetic moment solution as a function of the standard deviation of the triaxial magnetometer noise. Detailed Implementation

[0053] The present invention will now be further described with reference to the accompanying drawings.

[0054] This invention discloses a short baseline constraint optimization positioning method, measurement array, and system based on magnetic moment vector modulus ratio. The method includes the following steps:

[0055] Step 1, as follows Figure 1 As shown, using a regular triangular prism as the configuration of the measurement array can reduce the magnetic field measurement error caused by the finite baseline length of the array. This is because this configuration has a two-layer symmetrical structure in space. The regular triangular prism array consists of six triaxial magnetometers, which are located at points... , , , , and The triaxial magnetometers are numbered 1 to 6 sequentially. Align the sensing axes of the six triaxial magnetometers with each other. and All are equilateral triangles, and their circumradius is given by... Located in outer core and The midpoint of the line connecting the circumcenters is the origin O of the spatial rectangular coordinate system. shaft and The sides are parallel and by point to , shaft and The sides are parallel and by point to , The length of the side is .

[0056] A triaxial magnetometer is placed at each vertex of a regular triangular prism, meaning each vertex corresponds to a magnetic measurement point. The magnetometer will be located at... , , , , and The triaxial magnetometers are labeled 1 to 6, and the spatial coordinates of each measuring point are as follows: , , , , , .

[0057] Step 2: Simultaneously acquire the magnetic field measurement values ​​output by six triaxial magnetometers using a multi-channel data acquisition device, and calculate the origin of the coordinate system according to equations (1) and (2). magnetic field vector at the location and magnetic field vector magnitude gradient , and .

[0058] (1)

[0059] (2)

[0060] in, , , , , and This represents the measured value of the magnetic field vector. , , , , and B(O) represents the corresponding magnetic field vector magnitude; B(O) represents the magnetic field vector magnitude at point O.

[0061] Step 3: Using the spatial coordinates of the magnetic target Using the decision variables, a mathematical model is established for the constrained optimization problem of magnetic dipole positioning.

[0062] The objective function in this constrained optimization problem for:

[0063] (3)

[0064] in, ,and

[0065] (4)

[0066] in, , Indicates by the first The magnetic dipole magnetic moment vector magnitude calculated from the data of each measuring point ;constant , ρ is the magnetic permeability of the medium; Indicates the first Each measuring point relative to the magnetic target position vector, For the first Individual measuring points and magnetic targets The distance between them For the first Spatial coordinates of each measuring point; For magnetic targets in the first The magnetic field vector at each measuring point This represents the corresponding magnetic field vector magnitude.

[0067] The constraints in this constrained optimization problem are described as follows. Based on the characteristics of the magnetic field of the magnetic dipole, the inequality shown in equation (5) can be obtained.

[0068] (5)

[0069] in, express Spatial gradient, , Indicates magnetic target The distance between the measurement array and the origin O of the coordinate system.

[0070] Furthermore, based on the vertical position of the magnetic target relative to the regular triangular prism array and the establishment of the spatial coordinate system, the sign of the position coordinate components of the magnetic target is determined.

[0071] Step 4: Solve the constrained optimization problem in Step 3 using the adaptive differential evolution dung beetle optimization algorithm, and obtain the results. , and The optimal value.

[0072] Step 4.1 Algorithm Initialization:

[0073] a. Set the maximum number of algebras Defect coefficient , Reproduction area radius Initial mean of the variation factor Initial mean of crossover probability Adaptive update coefficients .

[0074] b. Assume the population size is... The problem dimension is The lower and upper bounds of the search space are respectively and A hybrid initialization strategy combining Chebyshev chaotic mapping and reverse learning was employed for population initialization. The hybrid mapping generated a population of [number missing]. Reverse learning generates the number of individuals The populations generated by these two methods are merged, boundary conditions are processed, and the better one is selected. Each individual serves as the final initial population.

[0075] The Chebyshev chaotic mapping formula is:

[0076] (6)

[0077] in, To be selected randomly, .

[0078] The formula for the reverse learning strategy is:

[0079] (7)

[0080] in, express The inverse solution.

[0081] Step 4.2: Iteration process of the main loop of the algorithm:

[0082] a. Pre-updated distribution and location of various dung beetle types

[0083] Based on a predetermined ratio of individuals of rolling dung beetles, reproductive dung beetles, thieving dung beetles, and foraging dung beetles, the locations of each type of dung beetle are pre-updated and boundary conditions are applied. The pre-updation formula for the location of rolling dung beetles is:

[0084] (8)

[0085] in, This represents the current iteration number. Indicates the first Only one dung beetle in the first Position information at the next iteration Indicates uniform distribution in Random numbers within the interval The expression is:

[0086] (9)

[0087] Among them, attenuation factor , Represents the Hardamard product. Indicates uniform distribution in Random numbers within the interval.

[0088] The formula for pre-updating the location of breeding dung beetles is:

[0089] (10)

[0090] in, This is the current local optimum. , , for Uniformly distributed within the interval 3D random vector.

[0091] The formula for pre-updating the position of the dung beetle is:

[0092] (11)

[0093] in, for A standard normal random vector.

[0094] The formula for pre-updating the location of dung beetles is:

[0095] (12)

[0096] in, and All are evenly distributed in Random numbers.

[0097] b. Adaptive differential evolutionary mutation:

[0098] Perform population variation operations according to equations (13) and (14).

[0099] (13)

[0100] (14)

[0101] Among them, subscript , and , The expression is:

[0102] (15)

[0103] in, To distribute evenly in Random numbers on the array.

[0104] c. Randomly select intersection points for the crossover operation, with the crossover probability... The calculation formula is:

[0105] (16)

[0106] in, These are random numbers that follow a standard normal distribution.

[0107] d. Use a greedy algorithm to select the global optimum, and update the initial mean of the mutation factor using both the Lehmer mean and the arithmetic mean. and the initial mean of the crossover probability .

[0108] Step 4.3 Algorithm Termination and Output

[0109] when When the time is up, the algorithm terminates and outputs the globally optimal solution. .

[0110] Step 5: Solve the spatial coordinates of the magnetic target. Substituting the value into equation (17), we can calculate the result from the first... The three components of the magnetic moment vector obtained from the magnetic field values ​​at each measuring point , and , ,

[0111] (17)

[0112] in, , , , .

[0113] Will , and Substituting into equation (18), the three components of the magnetic moment vector are calculated. , and .

[0114] (18)

[0115] Finally, the shape and type of the magnetic target are determined by the three components of the magnetic moment vector.

[0116] use , and These represent the absolute errors of the three components of the position coordinates, respectively. , and Let $\frac{1}{2}$ represent the absolute errors of the three components of the magnetic moment. Numerical experiments were conducted in the Matlab simulation environment. The three components of the magnetic moment of the magnetic target located at point Q are: , and The coordinates of point Q are (100m, 70m, -50m). Using... Figure 1 The triaxial magnetometer array shown measures the magnetic field generated by a magnetic target. The array baseline parameters are... and The measurements are 0.2m and 0.4m respectively.

[0117] The noise of the triaxial magnetometer on each axis is an independent Gaussian process with a mean of 0 and a standard deviation of [missing value]. The noise distribution characteristics were reflected using the Monte Carlo method 50 times. Increasing from 0 nT to 1 nT, the absolute error curves of the positioning solution and magnetic moment solution under different noise standard deviations are as follows: Figure 3 and Figure 4 As shown. By Figure 3 and Figure 4 It can be seen that the absolute error values ​​of both the location solution and the magnetic moment solution increase with... It increases with the increase of .

[0118] In particular, in some preferred embodiments of the present invention, a computer device is also provided, including a memory and a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the short baseline constraint optimization positioning method based on the magnetic moment vector modulus ratio described in any of the above embodiments.

[0119] In some other preferred embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program / instruction is stored, wherein when the computer program is executed by a processor, it implements the steps of the short baseline constraint optimization positioning method based on the magnetic moment vector modulus ratio described in any of the above embodiments.

[0120] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio, which will not be repeated here.

[0121] Computer-readable storage media encompass a variety of types, including persistent and non-persistent, portable and fixed. These media store information using different technologies, and the content can be machine instructions, data structures, program modules, or other types of data. Some typical examples of computer storage media include: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), various types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory and other storage technologies, optical storage media such as CD-ROM and digital video disc (DVD), magnetic storage devices such as magnetic tape and disks, and other non-transferable media used to store information accessible to computing devices. It is important to note that the computer-readable media described herein do not include temporary storage media, such as modulated data signals and carrier waves.

[0122] Those skilled in the art will further recognize that the operation of the module can be achieved using existing technical protocols or programs, without relying on new computer programs themselves. The units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0123] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0124] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A short baseline constraint optimization positioning method based on magnetic moment vector magnitude ratio, characterized in that, Includes the following steps: A regular triangular prism measurement array is constructed, and magnetic field vector data generated by the magnetic target at each vertex measurement point of the array are collected. The magnetic field vector and its vector magnitude gradient at the origin of the measurement array coordinates are calculated. Using the spatial coordinates of the magnetic target as the decision variable, a constrained optimization model is established for solving the magnetic dipole localization problem. The objective function is the magnetic moment vector modulus ratio function. The optimal spatial coordinates of the magnetic target are obtained by solving the constrained optimization model using the adaptive differential evolution dung beetle optimization algorithm. Based on the optimal spatial coordinates, the three components of the magnetic moment vector of the magnetic target are calculated by inversion.

2. The short baseline constraint optimization positioning method based on magnetic moment vector magnitude ratio according to claim 1, characterized in that, The regular triangular prism measuring array includes six triaxial magnetometers, located at the six vertices of the regular triangular prism, with each vertex corresponding to a magnetic measurement point. The two bases of the array are equilateral triangles with circumradius . The height of the regular triangular prism measuring array is The origin of the spatial rectangular coordinate system is the midpoint of the line connecting the circumcenters of the two base surfaces. The coordinate system has its y-axis parallel to one side of the bottom surface and points from one end of that side to the other, and its z-axis parallel to the edge and points from the lower bottom surface to the upper bottom surface.

3. The short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio according to claim 2, characterized in that, The origin of the calculation coordinates magnetic field vector at the location and its vector magnitude gradient , and , in, , , , , and This represents the measured value of the magnetic field vector. , , , , and This corresponds to the magnetic field vector magnitude; , , , , and The positions of the six triaxial magnetometers are indicated; B(O) represents the magnetic field vector magnitude at point O.

4. The short baseline constraint optimization positioning method based on magnetic moment vector magnitude ratio according to claim 3, characterized in that, Using the spatial coordinates of the magnetic target For the decision variables, establish the following constrained optimization model: Objective function for: in, The number of measurement points; , Indicates by the first The magnetic dipole magnetic moment vector magnitude calculated from the data of each measuring point Each vertex of the regular triangular prism measurement array corresponds to a measurement point. ; Indicates the first Each measuring point relative to the magnetic target position vector; constant , ρ is the magnetic permeability of the medium; Indicates the first Individual measuring points and magnetic targets The distance between them; For magnetic targets in the first The magnetic field vector at each measuring point This corresponds to the magnetic field vector magnitude; The constraints are: in, , Indicates magnetic target The distance between the measurement array coordinate origin O and the coordinate system origin O; express Spatial gradient.

5. The short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio according to claim 4, characterized in that, The adaptive differential evolution dung beetle optimization algorithm includes: (1) The population is initialized using a hybrid strategy of Chebyshev chaotic mapping and reverse learning; (2) The individuals in the initial population are divided into four categories: rolling crickets, breeding crickets, thieving crickets and foraging crickets, and their positions are pre-updated respectively; (3) Perform differential evolution mutation and crossover operations on the pre-updated population; (4) The global optimal solution is updated using a greedy selection strategy, and the mutation factor and crossover probability parameters are updated adaptively using the Lymer mean and arithmetic mean; (5) Repeat steps (2) to (4) until the preset maximum number of iterations is reached, and output the global optimal solution as the spatial coordinates of the magnetic target. .

6. The short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio according to claim 5, characterized in that, The spatial coordinates of the magnetic target based on the optimal solution Inversion calculation of the three components of the magnetic moment vector of the magnetic target , and , in, , and Indicates by the first The three components of the magnetic moment vector obtained from the magnetic field values ​​at each measuring point.

7. The short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio according to claim 6, characterized in that, The , and Specifically, it is expressed as follows: in, , , , .

8. The measurement array of the short baseline constraint optimization positioning method based on magnetic moment vector modulus ratio according to claim 7, characterized in that, The measuring array is a regular triangular prism array, consisting of six triaxial magnetometers; the bases of the two parallel equilateral triangles are... and Six triaxial magnetometers are located at the six vertices of the regular triangular prism. , , , , and At each location, numbered sequentially from 1 to 6, the sensing axes of the six triaxial magnetometers are aligned with each other; the circumradius of the bases of the two equilateral triangles is defined as... ; located in outer core and The midpoint of the line connecting the circumcenters is the origin O of the spatial rectangular coordinate system. shaft and The sides are parallel and by point to , shaft and The sides are parallel and by point to , The length of the side is , that is, the height of the regular triangular prism measuring array; The six vertices correspond to six magnetic measurement points, and the spatial coordinates of each measurement point are as follows: , , , , , .

9. A computer device system, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 7 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method according to any one of claims 7 to 8.

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