A distributed motion magnetic source positioning system

Abstract

This invention provides a distributed motion magnetic source positioning system, including a data acquisition node module, a node transmission module, and a processing module. The data acquisition node module is signal-connected to the node transmission module, which transmits the information acquired by the data acquisition node module to the processing module via wireless communication. The processing module includes a node position information processing module, a node magnetic field information processing module, and a target positioning module, which processes the acquired data and performs target positioning. This invention utilizes miniature attitude sensors to autonomously correct node data, effectively reducing the impact of platform sway; it also uses a GPS module to measure node positions, reducing the impact of human-induced node movement; the number of measurement nodes can be manually controlled, and the network configuration is diverse and controllable, making it convenient to use; furthermore, it utilizes a "moth to a flame" optimization algorithm to improve positioning speed, achieving target positioning within seconds.

Description

Technical Field

[0001] This invention belongs to the technical field of magnetic source positioning systems, specifically relating to a distributed moving magnetic source positioning system and positioning method. Background Technology

[0002] Due to the advantages of simple and small-sized magnetic measurement equipment, the method of locating targets using magnetic anomaly signals has attracted widespread attention. By establishing a magnetic field radiation model of the target and then using optimization algorithms to estimate unknown parameters, the target's location can be estimated relatively accurately.

[0003] However, traditional LM methods have certain requirements for initial values: when the initial values ​​are poor, the method may experience iterative divergence or false solutions. Furthermore, environmental and platform interference significantly affects the results: platform swaying increases background noise, thus degrading the positioning performance. When using existing systems for target localization, maintaining consistency in the states of all sensor nodes is crucial; otherwise, localization cannot be completed, which creates certain inconveniences for the use of the positioning system. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a distributed motion magnetic source positioning system and positioning method to address the shortcomings of the prior art, thereby solving the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a distributed motion magnetic source positioning system, including a data acquisition node module, a node transmission module and a processing module;

[0006] The data acquisition node module includes a miniature magnetic sensor, an attitude sensor, and a GPS module. The miniature magnetic sensor is used to acquire environmental magnetic field information, the attitude sensor is used to acquire the attitude information of the data acquisition node module, and the GPS module is used to acquire node location information.

[0007] The data acquisition node module is signal-connected to the node transmission module. The information acquired by the data acquisition node module is transmitted to the processing module via wireless communication through the node transmission module. The processing module includes a node location information processing module, a node magnetic field information processing module, and a target positioning module. The processing module completes the processing of the acquired data and the target positioning function.

[0008] Furthermore, the node location information processing module specifically involves the GPS module collecting the location information of each node, selecting one node as the origin of the local coordinate system, and establishing a geographic coordinate system ox. g y g z g :x g The axis points east along the local latitude line; y gThe axis points north along the local meridian; z g The axis points upward along the local geographical perpendicular and is aligned with x. g y g The axes form a right-handed rectangular coordinate system, and the coordinates of the remaining nodes are calculated based on latitude and longitude information.

[0009] Assuming node 1 is the origin of the coordinate system, and its GPS data is (lon0, lat0), then the coordinates of node i (loni, lati) are:

[0010] x i ≈R(lon i -lon0)

[0011] y i ≈R(lat i -lat0)

[0012] Where R is the Earth's average radius of 6371 km.

[0013] Furthermore, the node magnetic field information processing module includes node magnetic field data calibration processing and target magnetic anomaly signal extraction processing acquired by the node.

[0014] The node magnetic field data calibration process includes the following steps:

[0015] S101. Obtain magnetic field data B collected by the miniature magnetic sensor in the node system. s ;

[0016] S102. Obtain the attitude sensor output information in the node system: α heading angle, β pitch angle, γ roll angle;

[0017] S103. Calculate and obtain the attitude transformation matrix of this node:

[0018]

[0019] S104. Calculate magnetic field data in geographic coordinate system

[0020] Furthermore, the target magnetic anomaly signal extraction and processing acquired by the node assumes that the geomagnetic field at the node does not change during the measurement process. The background field B at the node's location is obtained by averaging the magnetic field measurements taken at the node's location. geo The magnetic anomaly data collected by the nodes are

[0021] H=BB geo .

[0022] Furthermore, the target localization module is optimized using the following formula:

[0023]

[0024] In the formula: F is the relationship matrix between the magnetic source target and the node position, with a size of 3N×3;

[0025] M is the magnetic source parameter (m x m y m z )' is a 3×1 vector;

[0026] H represents the magnetic field data synchronously collected by each node. i =(H ix H iy H iz The set is a 3N×1 vector;

[0027] The objective function E is a nonlinear function of the magnetic source position parameters (x, y, z), and the Moth to a Flame algorithm is used for optimization calculation.

[0028] Furthermore, in the moth-to-a-flame algorithm, the variable to be optimized is taken as the position of the moth in space. By changing the position vector of the moth, the algorithm iteratively seeks optimization in multidimensional space to obtain the global optimal solution that meets the requirements. A moth is a solution to the problem, and the light source is a relatively optimal solution. Each moth corresponds to a light source to avoid the algorithm getting stuck in local optima. As long as an initial value is randomly generated in the interval of variable distribution, the algorithm can quickly converge to the local region where the optimal solution of the objective function is located and quickly find the local optimal solution.

[0029] Furthermore, the moth-to-a-flame algorithm is as follows:

[0030] Initialize the parameters: number of moths n = 100, maximum number of iterations T = 500, upper limit of variables ub = (x u y u , z u m xu m yu m zu ), lower limit of variable lb = ((x l y l , z l m xl m yl m zl ), tolerance ε;

[0031] S201. Calculate the positions of n randomly scattered moths;

[0032] M(i, j) = (ub(i) - lb(i)) * rand() + lb(i); Start iterating;

[0033] S202. Calculate the number of flames based on the current iteration number (1).

[0034] S203. Reset the position of the trespassing moth to the spatial boundary value;

[0035] S204. Using the objective function E = (FM - H) T (FM-H), calculate the performance value OM for all moths;

[0036] S205, Flame Position F Selection: In the initial iteration, based on performance values... O In the next iteration, the moths M are arranged from best to worst and assigned to the flame F. In the subsequent iterations, the flame position and the moth position from the previous iteration are arranged from best to worst according to the performance value OM. The first fn solutions are selected as the new flame position F, and the optimal solution is taken as the optimal flame F*.

[0037] S206, Update the spiral flight path parameter r value The value of b is b = r + (1 - r) * rand();

[0038] S207, Update moth location M:

[0039] Calculate the distance D between the moth and the flame: D = |F j -M i |;

[0040] Update the moth's position M(i,j) = D i e bt cos(2πt)+F j ;

[0041] When the i-th moth chases the i-th flame, when i > fn, the moth chases the fh-th flame; the process is repeated.

[0042] S208. Determine if the iteration has ended: whether the number of iterations has reached the maximum number of iterations T or E < ε;

[0043] No, return to step S202;

[0044] Yes, output the result, and output the optimal flame value F* as the final solution.

[0045] Compared with the prior art, the present invention has the following advantages:

[0046] This invention features a highly integrated node detection device. The number of node modules can be increased or decreased according to actual needs, allowing for flexible processing. Each node operates as a system, and the measurement operation process is simple. It can use miniature magnetic sensors to autonomously correct node data, effectively reducing the impact of platform swaying. It can use GPS modules to measure node positions, reducing the impact of human movement of nodes. Simultaneously, the GPS module enables millisecond-level synchronization of magnetic measurement data from each node, improving positioning accuracy without increasing system complexity. Furthermore, it utilizes a "moth to a flame" optimization algorithm to effectively address the issue of local optima that traditional magnetic positioning methods are prone to, while also improving positioning speed, enabling target positioning within seconds. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the principle framework of the present invention.

[0048] 1-Data acquisition node module; 11-Miniature magnetic sensor; 12-Attitude sensor; 13-GPS module; 2-Node transmission module; 3-Processing module; 31-Node location information processing module; 32-Node magnetic field information processing module; 33-Target positioning module. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, the present invention provides a technical solution: a distributed motion magnetic source positioning system, characterized in that it includes a data acquisition node module 1, a node transmission module 2, and a processing module 3;

[0051] The data acquisition node module 1 includes a miniature magnetic sensor 11, an attitude sensor 12, and a GPS module 13. The miniature magnetic sensor 11 is used to acquire environmental magnetic field information, the attitude sensor 12 is used to acquire the attitude information of the data acquisition node module 1, and the GPS module 13 is used to acquire node location information.

[0052] The data acquisition node module 1 is signal-connected to the node transmission module 2. The node transmission module 2 includes a data transmission radio and a data transmission antenna. The information acquired by the data acquisition node module 1 is transmitted to the processing module 3 via wireless communication through the node transmission module 2. The processing module 3 includes a node location information processing module 31, a node magnetic field information processing module 32, and a target positioning module 33. The processing module 3 completes the processing of the acquired data and the target positioning function.

[0053] The node location information processing module 31 specifically involves the GPS module 13 collecting the location information of each node, selecting one node as the origin of the local coordinate system, and establishing a geographic coordinate system ox. g y g z g :x g The axis points east along the local latitude line; y g The axis points north along the local meridian; z g The axis points upward along the local geographical perpendicular and is aligned with x. g y g The axes form a right-handed rectangular coordinate system, and the coordinates of the remaining nodes are calculated based on latitude and longitude information.

[0054] Assuming node 1 is the origin of the coordinate system, and its GPS data is (lon0, lat0), then the coordinates of node i (loni, lati) are:

[0055] x i ≈R(lon i -lon0)

[0056] y i ≈R(lat i -lat0)

[0057] Where R is the Earth's average radius of 6371 km.

[0058] The node magnetic field information processing module 32 processes node magnetic field data calibration and target magnetic anomaly signal extraction from the node.

[0059] The node magnetic field data calibration process includes the following steps:

[0060] S101. Acquire the magnetic field data B collected by the miniature magnetic sensor 11 in the node system. s ;

[0061] S102. Obtain the output information of attitude sensor 12 in the node system: α heading angle, β pitch angle, γ roll angle;

[0062] S103. Calculate and obtain the attitude transformation matrix of this node:

[0063]

[0064] S104. Calculate magnetic field data in geographic coordinate system

[0065] The extraction and processing of the target magnetic anomaly signal acquired by the node assumes that the geomagnetic field at the node does not change during the measurement process. The background field B at the node's location is obtained by averaging the magnetic field measurements taken at the node's location.geo The magnetic anomaly data collected by the nodes are

[0066] H=BB geo .

[0067] The target positioning module 33 is optimized using the following formula:

[0068]

[0069] In the formula: F is the relationship matrix between the magnetic source target and the node position, with a size of 3N×3;

[0070] M is the magnetic source parameter (m x m y m z )′, is a 3×1 vector;

[0071] H represents the magnetic field data synchronously collected by each node. i =(H ix H iy H iz The set is a 3N×1 vector;

[0072] The objective function E is a nonlinear function of the magnetic source position parameters (x, y, z), and the Moth to a Flame algorithm is used for optimization calculation.

[0073] In the moth-to-a-flame algorithm, the variable to be optimized is the position of the moth in space. By changing the position vector of the moth, the algorithm iteratively seeks optimization in multidimensional space to obtain the global optimal solution that meets the requirements. A moth is a solution to the problem, while the light source is a relatively optimal solution. Each moth corresponds to a light source to avoid the algorithm getting trapped in local optima.

[0074] When there are enough moths and flames, the moths' flight can search most of the solution space, thus ensuring the algorithm's exploration capability. During the optimization process, the number of flames decreases with the increase of the number of iterations, allowing the moths to fully search the neighborhood space of better solutions, ensuring the algorithm's utilization capability. Therefore, the moth-to-flame algorithm has good global search capability. The optimization result is independent of the initial value. As long as an initial value is randomly generated in the interval of the variable distribution, the algorithm can quickly converge to the local region where the optimal solution of the objective function is located and quickly find the local optimum.

[0075] The specific algorithm for moths flying into a flame is as follows:

[0076] Initialize the parameters: number of moths n = 100, maximum number of iterations T = 500, upper limit of variables ub = (x u y u , z u mxu m yu m zu ), lower limit of variable lb = ((x l y l , z l m xl m yl m zl ), tolerance ε;

[0077] S201. Calculate the positions of n randomly scattered moths;

[0078] M(i, j) = (ub(i) - lb(i)) * rand() + lb(i); Start iterating;

[0079] S202. Calculate the number of flames based on the current iteration number (1).

[0080] S203. Reset the position of the trespassing moth to the spatial boundary value;

[0081] S204. Using the objective function E = (FM - H) T (FM-H), calculate the performance value OM for all moths;

[0082] S205, Flame position F selection: In the first iteration, the moths M are arranged from best to worst according to the performance value 0M and assigned to the flame F; in the subsequent iterations, the flame position and the moth position of the previous iteration are arranged from best to worst according to the performance value OM, and the first fn solutions are selected as the new flame position F. The optimal solution is taken as the optimal flame F*.

[0083] S206, Update the spiral flight path parameter r value The value of b is b = r + (1 - r) * rand();

[0084] S207, Update moth location M:

[0085] Calculate the distance D between the moth and the flame: D = |F j -M i |;

[0086] Update the moth's position M(i,j) = D i e bt cos(2πt)+F j ;

[0087] When the i-th moth chases the i-th flame, when i > fn, the moth chases the fn-th flame; this process is repeated.

[0088] S208. Determine if the iteration has ended: whether the number of iterations has reached the maximum number of iterations T or E < ε;

[0089] No, return to step S202;

[0090] Yes, output the result, and output the optimal flame value F* as the final solution.

[0091] In practical applications, a distributed motion magnetic source positioning system can be used for real-time magnetic field monitoring. Measurement nodes can be placed at any location, and the nodes can collect the magnetic field at the target location while simultaneously transmitting the data back to the master node for real-time observation.

[0092] The entire data acquisition node module is mounted on a non-magnetic support, with a GPS antenna at the top and a miniature magnetic sensor and attitude sensor at the bottom. The GPS module measures the node's position, the attitude sensor measures the module's attitude, and the miniature magnetic sensor measures the ambient magnetic field. This system can transmit the location information and measured magnetic information of the measurement site in real time, achieving real-time monitoring of the destination's magnetic field.

[0093] It can also be used to create a shipborne surface or underwater magnetic target positioning system using a distributed motion magnetic source positioning system. The data acquisition node module is placed on the measurement ship. The GPS module is used to measure the node position, the attitude sensor is used to measure the ship's rolling attitude to correct the measurement magnetic field, and the miniature magnetic sensor is used to measure the magnetic field. The node collects the magnetic field generated when the target passes by, and the target position can be deduced based on the target's motion state.

[0094] It can also utilize a distributed motion magnetic source positioning system to measure the positioning of single-land magnetic targets. Each node can be calibrated to a geomagnetic coordinate system using attitude sensor data. The position of each node is provided by the GPS module, allowing calculation of its location in the local geomagnetic coordinate system. When a target passes through the sensor array, each node collects the magnetic field generated by the target. This magnetic field can be used to locate the target with centimeter-level accuracy. The array can detect magnetic targets up to 50 meters away.

[0095] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A distributed moving magnetic source positioning system, characterized by, It comprises a data acquisition node module (1), a node transmission module (2) and a processing module (3). The data acquisition node module (1) comprises a micro magnetic sensor (11), an attitude sensor (12) and a GPS module (13), the micro magnetic sensor (11) is used for acquiring environmental magnetic field information, the attitude sensor (12) is used for acquiring attitude information of the data acquisition node module (1), and the GPS module (13) is used for acquiring node position information. The data acquisition node module (1) is signal-connected with the node transmission module (2), information acquired by the data acquisition node module (1) is transmitted to the processing module (3) through wireless communication by the node transmission module (2), the processing module (3) comprises a node position information processing module (31), a node magnetic field information processing module (32) and a target positioning module (33), and the processing module (3) is used for completing processing of acquired data and target positioning; The node position information processing module (31) is specifically that after the GPS module (13) collects position information of each node, one node is selected as a local coordinate system coordinate origin, a geographic coordinate system ox g y g z g : x g axis points east along the local meridian; y g axis points north along the local meridian; z g axis points up along the local geographic vertical and forms a right-handed rectangular coordinate system with x g , y g , and the rest of the node coordinate positions are converted according to the latitude and longitude information; Assume that node 1 is the coordinate origin, and its GPS data is (lon0, lat0), and the coordinates of node k-1 are (lon k , lat k ). ； ； where R is the mean earth radius of 6371 km, Ionk-i is the longitude of node k-1, latk-i is the latitude of node k-1 k k is the latitude of node k-1​ The node magnetic field information processing module (32) comprises node magnetic field data calibration processing and target magnetic anomaly signal extraction processing acquired by the node; The node magnetic field information processing module (32) comprises node magnetic field data calibration processing and target magnetic anomaly signal extraction processing acquired by the node; The node magnetic field data calibration processing comprises the following steps: S101、Obtain the magnetic field data B collected by the micro magnetic sensor (11) in the node system S ; S102, acquiring output information of an attitude sensor (12) in a node system: an alpha heading angle, a beta pitch angle and a gamma roll angle; S103, calculating a node attitude conversion matrix: ； S104, calculating the magnetic field data under the geographic coordinate system ; The target magnetic anomaly signal extraction processing collected by the node is that the geomagnetic field at the default node does not change in the measurement process, the average value of the magnetic field measurement at the position of the node is obtained, and the background field B geo of the position of the node is obtained The magnetic anomaly data collected by the node is ； The target positioning module (33) is optimized by the following formula: ； In the formula, F is a relationship matrix of a magnetic source target and a node position, and the size is 3N*3; M is the magnetic source parameter is a 3 x 1 vector; H is the magnetic field data collected synchronously by each node H is the magnetic field data collected synchronously by each node H is the magnetic field data collected synchronously by each node H is the magnetic Objective function E is a nonlinear function of magnetic source position parameters (r x , r y , r z ), and is calculated by using the firefly algorithm for optimization. In the firefly algorithm, the variables to be optimized are regarded as the positions of fireflies in space, and the position vectors of the fireflies are changed to constantly iterate and optimize in multi-dimensional space to obtain a global optimal solution meeting the requirements. A firefly is a solution to the problem, and a light source is a better solution to the problem. Each firefly corresponds to a light source to avoid the algorithm falling into a local optimum. Only a preliminary value is randomly generated in the interval of variable distribution, and the algorithm can quickly converge to the local area where the optimal solution of the objective function is located and quickly find a local optimal solution. The firefly algorithm is as follows: Initialize parameters: number of fireflies n = 100, maximum number of iterations T = 500, upper limit of variables ub = (x u ,y u ,z u ,m xu ,m yu ,m zu ), lower limit of variables lb = (x l ,y l ,z l ,m xl ,m yl ,m zl ), tolerance ε, where x u and x l are upper and lower limits of x component of source position coordinate, y u and y l are upper and lower limits of y component of source position coordinate, z u and z l are upper and lower limits of z component of source position coordinate, m xu ,m yu ,m zu are upper limits of three components of source magnetic moment, m xl ,m yl ,m zl are lower limits of three components of source magnetic moment; S201, calculating positions of n randomly scattered fireflies; , start iteration; S202、According to the current iteration number I, the number of flames is calculated ; S203, resetting positions of out-of-bound fireflies to space boundary values; S204, using the objective function , calculate the performance value OM of all the flying moths; S205, selecting a flame position F: in initial iteration, the firefly M is given to the flame F according to the performance value OM from good to bad; in the rest of iterations, the flame position and the firefly position of the last iteration are arranged from good to bad according to the performance value OM, the first fn solutions are selected as the new flame position F, and the optimal solution is selected as the optimal flame F*; S206, updating the spiral flight path parameter r value and b value ; S207, updating the firefly position M: Computing fly and flame distance ; updating moth position ; When the ith firefly chases the jth flame, when j>fn, the firefly chases the fnth flame; the iteration is circularly performed; S208, judging whether the iteration is ended: whether the iteration number reaches the maximum iteration number T or E<ε; no, returning to S202; yes, outputting a result, and outputting the optimal flame value F* as a final solution.

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