An underwater magnetic target positioning method based on tetrahedron scalar magnetometer array

Abstract

The present invention provides an underwater magnetic target positioning method based on a tetrahedron scalar magnetometer array. Four scalar magnetometers are used to form a regular tetrahedron detection array. The initial position coordinates, movement speed and movement direction of the magnetic moving target are obtained by inverting the magnetic field signals of the four scalar magnetometers, thereby positioning the magnetic moving target. The method has the characteristics of requiring a small number of magnetometers, fast solution speed and high solution accuracy. The method can be applied to various magnetic detection scenarios for target positioning, and is particularly suitable for positioning ships and underwater vehicles by submerged buoy platforms.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater target positioning, and in particular to an underwater magnetic target positioning method based on a tetrahedron scalar magnetometer array. Background Art

[0002] Seawater is a good medium for sound propagation, making sonar detection the primary technical means of underwater exploration. However, with the development of various anti-sonar technologies and the influence of ocean background noise, sonar detection efficiency has been greatly reduced. The geomagnetic field, as one of the Earth's intrinsic vector physical fields, is the result of the combined superposition of various magnetic fields generated by magnetic materials and dynamic processes in different structural components of the Earth's interior (core, mantle, and crust), as well as the Earth's internal and external current systems. The movement of magnetic targets causes changes in the spatial distribution of the geomagnetic field, generating magnetic field signals within the relatively stable geomagnetic field. These signals are difficult to eliminate and are unaffected by ocean background noise. Therefore, using magnetometers to measure magnetic field signals for magnetic target positioning has become a new underwater target positioning technology.

[0003] This technology is generally used on submerged buoy platforms. Affected by natural factors such as ocean currents and tides, the submerged buoys shake greatly. If a vector magnetometer is used, issues such as attitude, orientation, orthogonality, and steering difference need to be considered. However, a scalar magnetometer does not need to consider three-axis attitude calibration, and has higher sensitivity and accuracy, can achieve longer-distance detection, and is more suitable for submerged buoy platforms.

[0004] Because scalar magnetometers only provide scalar information about targets, multiple scalar magnetometers are currently used to form an array for joint detection to obtain more information. However, using more scalar magnetometers not only increases costs but also makes array deployment more difficult. Using fewer scalar magnetometers to construct a simple and feasible array, which allows for rapid and accurate magnetic moving target positioning, is of great significance for the application of magnetic moving target positioning technology. Summary of the Invention

[0005] The present invention provides a magnetic target positioning method based on a tetrahedron scalar magnetometer array. Four scalar magnetometers are used to form a detection array, and a magnetic moving target is quickly positioned according to position information and measured magnetic field data of the four scalar magnetometers.

[0006] The present invention provides an underwater magnetic target positioning method based on a tetrahedron scalar magnetometer array, comprising the following steps:

[0007] Step 1: Place four scalar magnetometers at preset positions to form a regular tetrahedron detection array, and measure the relative position coordinates of the four scalar magnetometers and their angles to magnetic north;

[0008] Step 2: Establish a right-handed rectangular coordinate system with the moving direction of the magnetic target as the x-axis and the vertical downward direction as the z-axis as the solution coordinate system;

[0009] Step 3: Take the magnetic target as a magnetic dipole to establish a magnetic dipole mathematical model. The magnetic moments in the three directions of the magnetic dipole are M x 、M y and M z , then the magnetic field generated by the magnetic target can be expressed as a matrix form H = FM, which can be expanded as:

[0010]

[0011] Among them, H c It is the superposition of the magnetic field generated by the magnetic dipole and the earth's magnetic field, that is, the scalar magnetic field actually measured by the magnetometer. f1, f2 and f3 are the coefficient matrices of the magnetic moments in the three directions of the magnetic dipole respectively. The specific calculation formula is:

[0012]

[0013] Among them, Φ is the geomagnetic inclination, α is the angle between the moving direction of the magnetic target and the magnetic north, and a x 、a y 、a z 、b x 、b y 、b z 、c x 、c y 、c z The solution formula is:

[0014]

[0015] in, x0, y0, z0 are the initial position coordinates of the magnetic target, v is the running speed of the magnetic target, T s is the sampling time of the magnetometer;

[0016] Step 4: Obtain the magnetic field data measured by the four scalar magnetometers according to their relative position coordinates, and construct four F = [F1, F2, F3, F4] T The matrix and four H = [H1, H2, H3, H4] T matrix;

[0017] Step 5: Use the F matrix and the H matrix to construct the objective function. The objective function is:

[0018]

[0019] Among them, || ||2 is the 2nd-order norm, F + is the generalized inverse matrix of the F matrix;

[0020] Step 6: Use the improved genetic algorithm to solve the objective function and obtain the initial position coordinates x0, y0, z0 of the magnetic target, the movement speed v, and the angle α between the movement direction and the magnetic north.

[0021] Optionally, the improved genetic algorithm includes the following steps:

[0022] Generate an initial F matrix, which is generated by a chaotic random function. Each parameter in the F matrix sets a boundary and sets the number of populations, where the number of populations is the number of F matrices.

[0023] Calculate individual fitness, bring all F matrices into the objective function to calculate the function value, and use the function value in the objective function as the individual fitness;

[0024] Selection, arrange the individuals from small to large according to their fitness, and use the random traversal sampling function to remove the F matrix with low fitness according to the preset generation gap, where the generation gap represents the intensity of the selection operation;

[0025] Crossover: parameterize each F matrix into a binary matrix of a certain length, and use the crossover operation to exchange 0 and 1 at random positions between two individuals to generate new individuals;

[0026] Mutation, using random functions to generate new F matrices to prevent the algorithm from converging locally.

[0027] The present invention provides a method and device for underwater magnetic target positioning based on a tetrahedron scalar magnetometer array. Four scalar magnetometers are used to form a regular tetrahedron detection array. The initial position coordinates, movement speed and movement direction of the magnetic moving target are obtained by inverting the magnetic field signals of the four scalar magnetometers, thereby positioning the magnetic moving target. The method has the characteristics of requiring a small number of magnetometers, fast solution speed and high solution accuracy. It can be applied to various magnetic detection scenarios for target positioning, and is particularly suitable for submerged buoy platforms to position ships and underwater vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 A schematic diagram of a scenario architecture on which the present disclosure is based;

[0030] Figure 2A schematic flow chart of an underwater magnetic target positioning method based on a tetrahedron scalar magnetometer array provided in an embodiment of the present disclosure;

[0031] Figure 3 This is a diagram of the arrangement of the scalar magnetometer array;

[0032] Figure 4 To solve the coordinate system top view;

[0033] Figure 5 are the magnetic field signal curves of four scalar magnetometers;

[0034] Figure 6 This is the flow chart of the improved genetic algorithm. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] Figure 1 A schematic diagram of a scenario architecture on which the present disclosure is based, such as Figure 1 As shown, a scenario architecture based on which the present disclosure is based may include a buoy platform 1 and a detection array 2 .

[0037] Among them, the submerged buoy platform 1 is a system moored below the sea surface for long-term observation of marine environmental elements, on which a detection array 2 can be set. The submerged buoy platform 1 can interact with the detection array 2 for executing the underwater magnetic target positioning method based on the tetrahedron scalar magnetometer array described in the following embodiment.

[0038] Specifically, the detection array 2 detects the magnetic field signal generated by the underwater target, and obtains the initial position coordinates, movement speed and movement direction of the magnetic moving target by inverting the magnetic field signal measured by the detection array 2, thereby locating the magnetic moving target, and the positioning information of the magnetic moving target can be uploaded to the ground.

[0039] The underwater magnetic target positioning method based on the tetrahedron scalar magnetometer array provided by this application is further described below:

[0040] This embodiment uses the simulation data of the magnetic dipole model to verify the magnetic target positioning effect, refer to Figure 2 , including the following steps:

[0041] Step 1: Construct a tetrahedron scalar magnetometer array, such as Figure 3As shown, the magnetometer spacing d needs to comprehensively consider the magnetometer detection capability and the magnetic moment size of the magnetic target;

[0042] In this embodiment, the distance between magnetometers is set to 50m.

[0043] Step 2: Establish the solution coordinate system, such as Figure 4 As shown in the figure, the coordinate system is solved with the magnetic target as the origin, the moving direction of the magnetic target is the x-axis, the vertical downward direction is the z-axis, α is the angle between the moving direction of the magnetic target and the magnetic north, θ is the angle between the line connecting magnetometer 1 and magnetometer 2 and the magnetic north, and d is the distance between the magnetometers. The position coordinates of the four magnetometers can be determined by α, θ and d.

[0044] In this embodiment, the coordinates of magnetometer 1 are (x, y, z), and the coordinates of magnetometer 2 are (x+d·cos(θ-α), yd·sin(θ-α), z), the coordinates of magnetometer 3 are (x+d·sin(30-θ+α), yd·cos(30-θ+α), z), and the coordinates of magnetometer 4 are

[0045] Step 3: Take the magnetic target as a magnetic dipole to establish a magnetic dipole mathematical model. The magnetic moments in the three directions of the magnetic dipole are M x 、M y and M z , then the magnetic field generated by the magnetic target can be expressed as a matrix form H = FM, which can be expanded as:

[0046]

[0047] Among them, H c It is the superposition of the magnetic field generated by the magnetic dipole and the earth's magnetic field, that is, the scalar magnetic field actually measured by the magnetometer. f1, f2 and f3 are the coefficient matrices of the magnetic moments in the three directions of the magnetic dipole respectively. The specific calculation formula is:

[0048]

[0049] Among them, Φ is the geomagnetic inclination, α is the angle between the moving direction of the magnetic target and the magnetic north, and a x 、a y 、a z 、b x 、b y 、b z 、c x 、c y 、c z The solution formula is:

[0050]

[0051] in, x0, y0, z0 are the initial position coordinates of the magnetic target, v is the running speed of the magnetic target, T s is the sampling time of the magnetometer.

[0052] In this embodiment, the magnetic dipole is set to have a magnetic moment in the running direction only, and the magnetic moment is 30000A·m 2 , the geomagnetic field is set to 48000nT, the geomagnetic inclination is 45°, the running speed of the magnetic target is set to 1m / s, the running direction is 0°, and the magnetometer sampling rate is 0.1Hz;

[0053] Step 4: Obtain the magnetic field data measured by the four scalar magnetometers according to their relative position coordinates, and construct four F = [F1, F2, F3, F4] T The matrix and four H = [H1, H2, H3, H4] T matrix;

[0054] In this embodiment, the magnetic field signals of four magnetometers are obtained by simulation, such as Figure 5 shown.

[0055] Step 5: Use the F matrix and the H matrix to construct the objective function. The objective function is:

[0056]

[0057] Among them, || ||2 is the 2nd-order norm, F + is the generalized inverse matrix of the F matrix;

[0058] In this step, if the matrix H = FM, then M = F + H, therefore FF + H=FM=H, when F is the ideal true value, the matrix H-FF + When H = 0, the norm of the objective function reaches the minimum. The denominator is a constant, which plays a role in enhancing the convergence of the objective function.

[0059] Step 6: Solve the objective function to obtain the initial position coordinates x, y, z of the magnetic target relative to the magnetometer 1, the movement speed v, and the angle α between the movement direction and the magnetic north;

[0060] In this step, the improved genetic algorithm includes: generating an initial F matrix, the initial F matrix is ​​generated by a chaotic random function, each parameter in the F matrix is ​​set with a boundary, and the number of populations is set, wherein the number of populations is the number of F matrices; calculating individual fitness, bringing all F matrices into the objective function to calculate the function value, and using the function value in the objective function as the individual fitness; selecting, arranging the individuals according to their fitness from small to large, and removing the F matrices with low fitness according to a preset generation gap using a random traversal sampling function, wherein the generation gap represents the strength of the selection operation; crossover, parameterizing each F matrix into a binary of a certain length, and exchanging 0 and 1 at random positions of two individuals through a crossover operation to generate new individuals; and mutation, generating a new F matrix using a random function to prevent the algorithm from converging locally.

[0061] In this embodiment, an improved genetic algorithm is used to solve the objective function. The algorithm flow is as follows: Figure 6 As shown in the figure, the population size of the genetic algorithm is set to 40, the generation gap is set to 0.9, the crossover probability is set to 0.7, the selection function uses the random traversal sampling function, and the crossover function uses the two-point crossover function. After multiple iterations, the optimal solution of the five variables is directly output. The final solution result is [x = 154.69m, y = 103.46m, z = 9.57m, v = 1.008m / s, α = -0.621°], and the theoretical value is [x = 150m, y = 100m, z = 10m, v = 1m / s, α = 0°]. The deviations of the position coordinates and speed are both less than 5%, and the running direction deviation is 0.621°.

[0062] Through the above-mentioned underwater magnetic target positioning method based on the tetrahedron scalar magnetometer array, four scalar magnetometers are used to construct a regular tetrahedron detection array to achieve three-dimensional positioning of moving magnetic targets and minimize the number of magnetometers; an objective function that does not contain three-directional magnetic moments is constructed to speed up the solution by reducing the number of solution variables; an improved genetic algorithm is used to solve the objective function to avoid the function converging to the local optimal solution and improve the solution accuracy.

[0063] Although the present invention has been disclosed above by way of embodiments, they are not intended to limit the present invention. Any person skilled in the art may make slight changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for underwater magnetic target positioning based on a tetrahedron scalar magnetometer array, characterized in that: The following steps are involved: Step 1: Place four scalar magnetometers at preset positions to form a regular tetrahedron detection array, and measure the relative position coordinates of the four scalar magnetometers and their angles to magnetic north; Step 2: Establish a right-handed rectangular coordinate system with the moving direction of the magnetic target as the x-axis and the vertical downward direction as the z-axis as the solution coordinate system; Step 3: Take the magnetic target as a magnetic dipole to establish a magnetic dipole mathematical model. The magnetic moments in the three directions of the magnetic dipole are M x 、M y and M z , then the magnetic field generated by the magnetic target can be expressed as a matrix form H = FM, which can be expanded as: Among them, H c It is the superposition of the magnetic field generated by the magnetic dipole and the earth's magnetic field, that is, the scalar magnetic field actually measured by the magnetometer. f1, f2 and f3 are the coefficient matrices of the magnetic moments in the three directions of the magnetic dipole respectively. The specific calculation formula is: Among them, Φ is the geomagnetic inclination, α is the angle between the moving direction of the magnetic target and the magnetic north, a x 、a y 、a z 、b x 、b y 、b z 、c x 、c y 、c z The solution formula is: in, x0, y0, z0 are the initial position coordinates of the magnetic target, v is the moving speed of the magnetic target, T s is the sampling time of the magnetometer; Step 4: Obtain the magnetic field data measured by the four scalar magnetometers according to their relative position coordinates, and construct four F = [F1, F2, F3, F4] T The matrix and four H = [H1, H2, H3, H4] T matrix; Step 5: Use the F matrix and the H matrix to construct the objective function. The objective function is: Among them, || ||2 is the 2nd-order norm, F + is the generalized inverse matrix of the F matrix; Step 6: Use the improved genetic algorithm to solve the objective function and obtain the initial position coordinates x0, y0, z0 of the magnetic target, the movement speed v, and the angle α between the movement direction and the magnetic north.

2. The underwater magnetic target positioning method based on a tetrahedron scalar magnetometer array according to claim 1, characterized in that: The improved genetic algorithm comprises the following steps: Generate an initial F matrix, which is generated by a chaotic random function. Each parameter in the F matrix sets a boundary and sets the number of populations, where the number of populations is the number of F matrices. Calculate individual fitness, bring all F matrices into the objective function to calculate the function value, and use the function value in the objective function as the individual fitness; Selection, arrange the individuals from small to large according to their fitness, and use the random traversal sampling function to remove the F matrix with low fitness according to the preset generation gap, where the generation gap represents the intensity of the selection operation; Crossover: parameterize each F matrix into a binary matrix of a certain length, and use the crossover operation to exchange 0 and 1 at random positions between two individuals to generate new individuals; Mutation, using random functions to generate new F matrices to prevent the algorithm from converging locally.

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