A real-time positioning method and system for a vector sensor network based on magnetic moment invariants

Abstract

The application discloses a kind of vector sensor network real-time positioning method and system based on magnetic moment invariant, belongs to magnetic anomaly detection field, solve the problem that actual positioning performance is low in traditional network positioning method, real-time and positioning accuracy is poor.The technical points of the present application are as follows: in the positioning of distributed magnetic sensor network, the real-time magnetic field information observed by the magnetic sensor node is inverted, and a method for constructing a cost function with fewer parameters is proposed based on the magnetic moment invariant, reducing the number of parameters to be solved.The present application is suitable for actual outdoor positioning scene.

Description

Technical Field

[0001] This invention belongs to the field of magnetic anomaly detection, specifically relating to a vector magnetic sensing network for localization. Background Technology

[0002] 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 detecting and locating hidden targets, especially in cross-media detection scenarios.

[0003] Currently, target location analysis based on magnetic anomaly detection often employs vector magnetic sensor networks, using inversion algorithms to solve for the magnetic anomaly information. With the recent trend towards miniaturization and lower cost of magnetic sensors, large-scale application of sensor networks for target detection has become possible. Traditional full-parameter localization methods are mostly used in indoor scenarios such as medical settings, employing five or more sensor nodes for localization. However, in outdoor scenarios, sensor deployment is often random and sparse, resulting in lower actual localization performance. Furthermore, when the magnetic anomaly signal source is equivalent to a magnetic dipole, the target location parameter inversion requires solving for six parameters, including three positional quantities and three magnetic moments. Traditional full-parameter inversion algorithms solve for all six parameters simultaneously, leading to poor real-time performance and positioning accuracy. Therefore, methods with high real-time performance and applicability under sparse network conditions are needed for practical outdoor positioning scenarios.

[0004] For constructing the cost function, the conventional inversion algorithm is full parameter solution (APS), which involves constructing a cost function with six parameters (three position parameters and three magnetic moment parameters). Therefore, the cost function F of the APS algorithm... APS It can be defined in the following form:

[0005]

[0006] The superscript 'e' represents the estimated value.

[0007] Since this strategy requires solving for six parameters simultaneously, it necessitates constructing a system of equations containing at least six nonlinear equations, meaning at least two triaxial magnetic sensors are needed for the solution. However, the timeliness of this cost function is poor. To address this issue, related literature suggests improving the optimization algorithm, such as using a combination of PSO and LM algorithms to enhance performance. Furthermore, in practical applications, to achieve better positioning accuracy, such as in small-scale capsule endoscopy positioning and tracking research, more than five triaxial sensors are used for positioning, improving solution performance by increasing the amount of information from the magnetic sensors. However, this method is difficult to apply to real-time positioning applications with large-scale sparse sensor node networks. The reason is that improving the optimization algorithm and increasing the number of sensors do not change the fundamental requirement of the APS algorithm to perform parameter search in a 6-dimensional solution space. Because a large number of extreme points exist in the high-dimensional solution space, the solution is prone to getting trapped in local optima. Therefore, separating position and magnetic moment quantities and constructing a cost function in a low-dimensional solution space is crucial for improving real-time positioning performance. Summary of the Invention

[0008] This invention provides a real-time positioning method and system based on a vector sensor network with magnetic moment invariant, which solves the problems of low actual positioning performance, poor real-time performance and poor positioning accuracy of traditional network positioning methods.

[0009] A real-time localization method for vector sensor networks based on magnetic moment invariants, the method comprising:

[0010] Establish a positioning analysis model based on the target;

[0011] The cost function is defined based on the principle of magnetic moment uniformity.

[0012] The target's location information and the magnetic field information at the corresponding location points are obtained based on the model.

[0013] Substitute the target's location information and the magnetic field information at the corresponding location point into the cost function;

[0014] Based on the model, an optimized solution algorithm is selected to obtain multiple sets of magnetic moment solutions and iterations for the target.

[0015] Positioning information is obtained based on the differences between multiple sets of magnetic moment quantities;

[0016] The magnetic moment of the target is obtained by averaging multiple sets of magnetic moment quantities based on the positioning information, thus completing a real-time positioning method for vector sensor networks based on magnetic moment invariants.

[0017] Furthermore, the objective is to represent the actual positioning target as an equivalent magnetic dipole, with the magnetic field represented as follows:

[0018]

[0019] Where μ0 = 4π × 10 -7 H / m is the free permeability, r = [x, y, z] represents the relative position of the magnetic dipole and the observation point, and m = [m x ,m y ,m z ] represents the magnetic moment of the target.

[0020] Furthermore, the magnetic field is expressed as B = kAm.

[0021] Where k is a constant term μ0 / 4π, and A is the position vector matrix.

[0022] Furthermore, the position vector matrix is ​​specifically:

[0023]

[0024] Furthermore, the cost function is:

[0025]

[0026] in, m xi e (i = 1, 2, ..., n) represents the estimated magnetic moment value of the x-axis calculated by the i-th sensor, m yi e and m zi e y and z are the estimated magnetic moment values ​​of the i-th sensor, respectively.

[0027] Furthermore, the differences among the multiple sets of magnetic moments are represented by the standard deviation function σ.

[0028] Furthermore, the magnetic moment of the target is:

[0029]

[0030] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the above-described real-time positioning method for a vector sensor network based on magnetic moment invariants.

[0031] A computer-readable storage medium for storing a computer program that executes the above-described real-time positioning method for a vector sensor network based on magnetic moment invariants.

[0032] The real-time positioning method for vector sensor networks based on magnetic moment invariants described in this invention can be entirely implemented using computer software. Therefore, correspondingly, this invention also claims a real-time positioning system for vector sensor networks based on magnetic moment invariants, the system comprising:

[0033] A modeling device used to establish a positioning analysis model based on a target;

[0034] A device used to define the cost function;

[0035] A receiving device for obtaining the location information of a target and the magnetic field information at the corresponding location point;

[0036] A computing device for substituting the target's location information and the magnetic field information at the corresponding location point into the cost function, selecting an optimized solution algorithm, and obtaining multiple sets of magnetic moment quantities of the target for solution and iteration;

[0037] A calculation device for obtaining positioning information based on the differences between multiple sets of magnetic moments, and for averaging the multiple sets of magnetic moments to obtain the target magnetic moment.

[0038] The beneficial effects of this invention are:

[0039] This invention proposes a real-time localization method and system based on magnetic moment invariants for vector sensor networks. This method offers faster solution speed and higher accuracy, significantly improving both real-time performance and accuracy compared to traditional APS algorithms, making it more significant in practical applications. This advantage stems from the construction of the cost function, which reduces the number of parameters to be solved. With fewer parameters, the solution space of the cost function is optimized, greatly improving localization accuracy. Monte Carlo simulations show that, under the same conditions (100 search particles), the traditional algorithm achieves approximately 20%, 35%, and 50% accuracy in solution times of 0.1s, 0.3s, and 1.0s, respectively, while the method described in this invention achieves 30%, 95%, and 99% accuracy, respectively. This comparison demonstrates that the accuracy of the method described in this invention is significantly improved compared to existing technologies.

[0040] This invention is applicable to practical outdoor positioning scenarios. Attached Figure Description

[0041] Figure 1 This refers to the distributed sensor network observation model described in Implementation Method 1;

[0042] Figure 2 This is a flowchart illustrating the real-time positioning method of a vector sensor network based on magnetic moment invariants as described in Embodiment 1 and Embodiment 2. Detailed Implementation

[0043] Implementation Method 1: Refer to Figure 1 , Figure 2 This implementation method is described below.

[0044] This embodiment describes a real-time positioning method for vector sensor networks based on magnetic moment invariants, the method comprising:

[0045] Establish a positioning analysis model based on the target;

[0046] The cost function is defined based on the principle of magnetic moment uniformity.

[0047] The target's location information and the magnetic field information at the corresponding location points are obtained based on the model.

[0048] Substitute the target's location information and the magnetic field information at the corresponding location point into the cost function;

[0049] Based on the model, an optimized solution algorithm is selected to obtain multiple sets of magnetic moment solutions and iterations for the target.

[0050] Positioning information is obtained based on the differences in multiple sets of magnetic moment quantities;

[0051] The magnetic moment of the target is obtained by averaging multiple sets of magnetic moment quantities based on the positioning information, thus completing a real-time positioning method for vector sensor networks based on magnetic moment invariants.

[0052] Specifically:

[0053] like Figure 1 The diagram shows a distributed sensor network observation model composed of n magnetic sensors to detect magnetic anomalies in a target. Within the same observation coordinate system C, the sensors located at s... i A sensor observation network consisting of n triaxial magnetic sensors at points (i = 1, 2, ..., n). When a magnetic dipole exists at point p, the following equation applies:

[0054]

[0055] This allows us to determine the magnetic field value in the observation coordinate system at each observation point. Considering the randomness of the vector sensor's orientation during node arrangement, a rotation matrix R is used to represent the orientation of each triaxial sensor, thus obtaining the actual observed magnetic field of the i-th sensor:

[0056]

[0057] Among them G i Let N be the geomagnetic field component observed by the i-th sensor. In the short term, this can be considered a constant and can be removed in subsequent signal processing using previous observations. iThe corresponding background noise is used for observation; for the random attitude R of the sensor, the corresponding attitude of the sensor can be specified when the sensor is deployed or the corresponding attitude information can be obtained by equipping an attitude sensor.

[0058] Without considering observation noise N i Under these conditions, the observation field is preprocessed accordingly. In the observation coordinate system C, the magnetic anomaly information of the i-th sensor can be obtained using the following formula:

[0059] B i (m,r i ):

[0060]

[0061] Based on this model, the position and magnetic moment of the target object can be inverted using the sensor's position information and the preprocessed magnetic field information.

[0062] observe Figure 1 With formula

[0063]

[0064] It is known that for different sensors, the magnetic moment of the magnetic dipole is consistent in the magnetic anomaly information they perceive at the same time and in the same coordinate system. Therefore, by treating the magnetic moment information as a known invariant, the Magnetic Moment Invariant (MMI) algorithm is proposed to reduce the search dimension of the magnetic positioning problem.

[0065] The magnetic moment invariant algorithm proposed in this embodiment has a faster solution speed and higher solution accuracy when performing positioning. It has a significant improvement over the traditional APS algorithm in both positioning real-time performance and positioning accuracy.

[0066] Implementation Method Two: Refer to Figure 2 This implementation method is described below.

[0067] This embodiment is a further illustrative example of the target described in the real-time positioning method of a vector sensor network based on magnetic moment invariant described in Embodiment 1.

[0068] The objective of this embodiment is to represent the positioning target as an equivalent magnetic dipole, with the magnetic field represented as follows:

[0069]

[0070] Where μ0 = 4π × 10 -7 H / m is the free permeability, r = [x, y, z] represents the relative position of the magnetic dipole and the observation point, and m = [m x ,my ,m z ] represents the magnetic moment of the target.

[0071] Specifically:

[0072] As can be seen from the above equation, there is a nonlinear coupling relationship between the position quantity r and the magnetic moment quantity m, making it difficult to apply the equation to other equations.

[0073]

[0074] Elimination is performed to extract the independent position quantity *r* or magnetic moment quantity *m*, therefore this positioning problem often reduces to an optimization problem. The solution process of the entire method is as follows: Figure 2 As shown, under the condition that the observation information and the solution model are known, the key to the target localization solution is the construction of the cost function and the selection of the solution algorithm, which will directly affect the speed and accuracy of the solution.

[0075] Implementation Method 3:

[0076] This embodiment is a further illustrative example of the magnetic field representation described in the real-time positioning method of a vector sensor network based on magnetic moment invariants described in Embodiment 2.

[0077] The magnetic field described in this embodiment is represented as:

[0078] B = kAm,

[0079] Where k is a constant term μ0 / 4π, and A is the position vector matrix.

[0080] Specifically: The magnetic field representation described in this embodiment is first converted into the following form:

[0081]

[0082] Further simplified to

[0083] B = kAm.

[0084] If the location information of the target object and the magnetic field information at the corresponding location point are known, the magnetic moment of the target object can be calculated as follows:

[0085] m = A -1 B / k

[0086] Implementation Method Four:

[0087] This embodiment is a further illustrative example of the position vector matrix described in the real-time positioning method of a vector sensor network based on magnetic moment invariant described in Embodiment 3.

[0088] The position vector matrix described in this embodiment is specifically:

[0089]

[0090] Implementation Method 5:

[0091] This embodiment is a further illustrative example of the cost function described in the real-time localization method for vector sensor networks based on magnetic moment invariants described in Embodiment 1.

[0092] The cost function described in this embodiment is:

[0093]

[0094] in, m xi e (i = 1, 2, ..., n) represents the estimated magnetic moment value of the x-axis calculated by the i-th sensor, m yi e and m zi e y and z are the estimated magnetic moment values ​​of the i-th sensor, respectively.

[0095] Specifically:

[0096] Depend on

[0097] m = A -1 B / k

[0098] It can be known that:

[0099]

[0100] As can be seen from the above analysis, in the optimization problem described above, the magnetic moment is defined as a constant, and only the position quantity needs to be solved. The number of solution parameters for this problem is reduced from 6 to 3. It is foreseeable that the cost function constructed using the MMI principle described in this embodiment can improve the search speed and reduce the complexity of the search space.

[0101] Implementation Method Six:

[0102] This embodiment is a further example illustrating the differences in multiple sets of magnetic moment quantities in the real-time positioning method of a vector sensor network based on magnetic moment invariants described in Embodiment 1.

[0103] The differences among the multiple sets of magnetic moment quantities described in this embodiment are represented by the standard deviation function σ.

[0104] Specifically:

[0105] exist Figure 1 Under the observation conditions of the sensor network shown, at a certain estimated location p e n sets of magnetic moment values ​​m1 can be obtained at this location. e m2 e ,…mn e According to the MMI principle, theoretically p e For a correct estimate, we have m1 e =m2 e ,…=m n e Therefore, the cost function F of the MMI algorithm can be defined by the differences between multiple magnetic moment values. MMI The difference between magnetic moment values ​​is represented by the standard deviation function σ.

[0106] Implementation Method Seven:

[0107] This embodiment is a further illustrative example of the magnetic moment of the target in the real-time positioning method of a vector sensor network based on magnetic moment invariant described in Embodiment 1.

[0108] The magnetic moment of the target described in this embodiment is:

[0109] Specifically:

[0110] After solving for the positional quantities, the formula can be modified.

[0111]

[0112] The average of the calculated magnetic moments is used to solve for the magnetic moment of the target object.

[0113] Implementation Method Eight:

[0114] This embodiment describes a real-time positioning system based on a vector sensor network with magnetic moment invariants. The system includes:

[0115] A modeling device used to establish a positioning analysis model based on a target;

[0116] A device used to define the cost function;

[0117] A receiving device for obtaining the location information of a target and the magnetic field information at the corresponding location point;

[0118] A computing device for substituting the target's location information and the magnetic field information at the corresponding location point into the cost function, selecting an optimized solution algorithm, and obtaining multiple sets of magnetic moment quantities of the target for solution and iteration;

[0119] A calculation device for obtaining positioning information based on the differences between multiple sets of magnetic moments, and for averaging the multiple sets of magnetic moments to obtain the target magnetic moment.

Claims

1. A real-time positioning method for vector sensor network based on magnetic moment invariants, characterized in that, The method comprises: establishing a positioning analysis model according to a target; defining a cost function according to a magnetic moment consistency principle, the cost function being: ， in, , m xi e ( i = 1, 2,… n ) is the first i The sensor calculates x The estimated magnetic moment value of the shaft. m yi e and m zi e The first i Estimated magnetic moment values ​​for the y-axis and z-axis of each sensor; obtaining position information of the target and magnetic field information at a corresponding position point according to the model; substituting the position information of the target and the magnetic field information at the corresponding position point into the cost function; selecting an optimization solving algorithm according to the model to obtain a plurality of sets of magnetic moment quantity solutions and iterations of the target; obtaining positioning information according to differences between the plurality of sets of magnetic moment quantities; averaging the plurality of sets of magnetic moment quantities according to the positioning information to obtain a magnetic moment quantity of the target, thereby completing a real-time positioning method of a vector sensor network based on a magnetic moment invariant.

2. The real-time positioning method of vector sensor network based on magnetic moment invariant according to claim 1, characterized in that, The target is equivalent to a magnetic dipole, and the magnetic field is represented as follows: wherein The magnetic field is represented as: 0 = 4πx10 -7 H / m is the vacuum permeability, r = [ x , y , z ] denotes the relative position relationship between the magnetic dipole and the observation point, m =[ m x , m y , m z ] is the magnetic moment of the target.

3. The real-time positioning method of vector sensor network based on magnetic moment invariant according to claim 2, characterized in that, The position vector matrix is specifically: ， wherein k is a constant term The magnetic moment quantity of the target is: 0 / 4π, A is a position vector matrix.

4. The real-time positioning method of vector sensor network based on magnetic moment invariant according to claim 3, characterized in that, A memory and a processor are included, and the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the real-time positioning method of the vector sensor network based on the magnetic moment invariant according to any one of claims 1-6. 。 5. The real-time localization method of a vector sensor network based on magnetic moment invariant according to claim 1, characterized in that, The difference of the plurality of sets of magnetic moment quantities is represented by a standard deviation function The computer readable storage medium is used to store a computer program, and the computer program executes the real-time positioning method of the vector sensor network based on the magnetic moment invariant according to any one of claims 1-6. .

6. The real-time positioning method of vector sensor network based on magnetic moment invariant according to claim 1, characterized in that, The system comprises: 。 7. A computer device, comprising: a modeling device for establishing a positioning analysis model according to a target; 8. A computer-readable storage medium, characterized in that, a device for defining a cost function, the cost function being:

9. A real-time positioning system for a vector sensor network based on magnetic moment invariants, characterized by a receiving device for obtaining position information of the target and magnetic field information at a corresponding position point; a calculation device for substituting the position information of the target and the magnetic field information at the corresponding position point into the cost function, and selecting an optimization solving algorithm to obtain a plurality of sets of magnetic moment quantity solutions and iterations of the target; a calculation device for obtaining positioning information according to differences between the plurality of sets of magnetic moment quantities, and averaging the plurality of sets of magnetic moment quantities to obtain a magnetic moment quantity of the target. ， in, , m xi e ( i = 1, 2,… n ) is the first i The sensor calculates x The estimated magnetic moment value of the shaft. m yi e and m zi e The first i Estimated magnetic moment values ​​for the y-axis and z-axis of each sensor; ​ ​ ​

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