CN-122015812-A - Magnetic field odometer method and device based on magnetic tensor measurement array

CN122015812ACN 122015812 ACN122015812 ACN 122015812ACN-122015812-A

Abstract

The invention discloses a magnetic field odometer method based on a magnetic tensor measurement array, which comprises the following steps of S1, measuring the magnetic tensor of the position where the magnetic tensor measurement array is located by using the magnetic tensor measurement array, and S2, updating the position by adopting an analytical positioning method, a magnetic odometer algorithm or a self-adaptive analytical positioning and magnetic odometer fusion algorithm. The invention also discloses a magnetic field odometer device based on the magnetic tensor measurement array, which comprises the magnetic tensor measurement array, a memory and a processor, wherein the magnetic tensor measurement array is used for measuring magnetic tensor data of a position, the memory is used for storing the magnetic tensor data and a computer program, and the processor is used for executing the computer program to realize the steps of the magnetic field odometer method. According to the invention, an analytic positioning method and/or a magnetic mileage calculation method are adopted, and two algorithm self-adaptive weighting fusion strategies are further adopted, so that high-precision autonomous navigation under the assistance of a complete independent inertial navigation system and a priori geomagnetic map without a global geomagnetic field model is realized, and the long-time stability of positioning in a complex magnetic field environment is improved.

Inventors

WANG YICHEN
GAO DONG
BIAN CHUNJIANG

Assignees

中国科学院国家空间科学中心

Dates

Publication Date: 20260512
Application Date: 20260210

Claims (8)

1. A magnetic field odometer method based on a magnetic tensor measurement array, comprising the steps of: Step S1, measuring magnetic tensors of the positions by using a magnetic tensor measuring array; Step S2, the position of the magnetic tensor measurement array is updated by adopting the following mode one or the mode two: the first mode is an analytical positioning method, wherein the position update equation is as follows: Wherein, the To be located in the magnetic dipole field A magnetic gradient tensor matrix of locations; Is that Magnetic induction intensity of the location; the second mode is a magnetic mileage calculation method, wherein the position update equation is as follows: Wherein the method comprises the steps of 。
2. The method of claim 1, further comprising performing a position update of the magnetic tensor measurement array in step S2 by: The third mode is that the self-adaptive analysis positioning and magnetic odometer fusion algorithm comprises the following position update equation: Wherein the method comprises the steps of And To distinguish the position increment obtained by the analytic positioning method and the magnetic mileage calculation method, For a set maximum reasonable single step displacement, For adaptive weights, the expression is: Wherein the method comprises the steps of As the condition number of the average tensor matrix, Is the set maximum condition number.
3. The method of claim 1 or 2, wherein the magnetic tensor measurement array comprises four magnetic sensors respectively mounted at four end points of a cross array, intersecting lines of the cross array are orthogonal, and distances from the four magnetic sensors to the intersecting points of the intersecting lines are equal.
4. The magnetic field odometer method of claim 2, wherein the maximum number of conditions is set in accordance with a signal to noise ratio of the magnetic gradient tensor.
5. The magnetic field odometry of claim 4, wherein the maximum number of conditions is set to: Wherein the method comprises the steps of Is the signal to noise ratio of the magnetic gradient tensor.
6. A magnetic field odometer device based on a magnetic tensor measurement array, comprising: the magnetic tensor measuring array is used for measuring magnetic tensor data of the position; a memory for storing the magnetic tensor data and a computer program; A processor for executing the computer program to perform the steps of the magnetic field odometry method of claim 1.
7. The magnetic field odometer device of claim 6, wherein the magnetic tensor measurement array comprises four magnetic sensors mounted at four ends of the cross array, respectively, the cross lines of the cross array being orthogonal, the four magnetic sensors being equidistant from the intersection points of the cross lines.
8. A readable storage medium having stored thereon a computer program executable by a processor to perform the steps of the magnetic field odometry method of claim 1.

Description

Magnetic field odometer method and device based on magnetic tensor measurement array Technical Field The application relates to the technical field of navigation, in particular to a magnetic field odometer method and a device based on a magnetic tensor measurement array. Background Geomagnetic navigation is a way of navigating by using the information of the earth's magnetic field. By means of the characteristics that the geomagnetic field is relatively stable and changes relatively obviously with space, geomagnetic navigation can realize global all-weather work. Traditional geomagnetic navigation mostly depends on a priori geomagnetic map, and the position of the geomagnetic navigation is determined by establishing a mapping relation between a measured value of a magnetometer and the geomagnetic map. However, acquiring the map of the earth magnetic field is a time-consuming and labor-consuming task, so that it is difficult to acquire a large-scale map of the earth magnetic field, and the application range of geomagnetic navigation is limited. Magnetic field navigation methods that do not rely on prior magnetic field patterns can be broadly classified into magnetic field SLAM, heuristic geomagnetic navigation, data-driven geomagnetic navigation, and magnetic field gradient navigation according to their principles. The magnetic field SLAM is an autonomous navigation technology for synchronously realizing pose estimation and magnetic field distribution map construction by acquiring magnetic induction intensity change characteristics generated by a carrier in a space motion process in real time under the environment without a priori magnetic map. Taylor N.Lee et al, 2019, published MagSLAM: aerial simultaneous localization AND MAPPING using Earth' S MAGNETIC anomaly field in the Journal of the Institute of Navigation journal, which describes a MagSLAM algorithm that effectively combines pose estimation of an aircraft with a continuous geomagnetic anomaly map based on Gaussian process regression by improving the Rao-Blackwellized particle filter architecture, and introduces a selective resampling strategy to maintain particle diversity. In a flight test of a range of 9 multiplied by 12km for 100 minutes, the algorithm limits the accumulated drift of the inertial navigation system to be within a root mean square error range of 10-20 meters. This approach and other mainstream magnetic field SLAMs are mainly limited by the strong dependence on trajectory loops, resulting in an inability to effectively suppress drift in unidirectional tasks lacking revisit paths. Meanwhile, the calculation complexity caused by Gaussian process modeling enables calculation power cost to be obviously increased along with the time length. In the aspect of heuristic magnetic field navigation, 2018 Wang Qiong et al published a parallel approach geomagnetic gradient bionic navigation method on the university of northwest industry university report, and the article proposes a geomagnetic gradient bionic navigation algorithm based on the parallel approach method. According to the method, the sensitivity of the carrier pigeon to the magnetic field spatial gradient is simulated, and the nonlinear guidance law is constructed by utilizing gradient information of geomagnetic vector components, so that the capability of the carrier for approaching and growing Cheng Dingxiang of the target under the condition that no priori magnetic diagram is needed is realized. The algorithm is highly dependent on the significance of local magnetic field characteristics, is easy to fail in a magnetic field gradient stable region, is mainly limited to two-dimensional space guidance, and does not completely solve the problem of perception uncertainty caused by the fact that a magnetic field is attenuated along with the height in a three-dimensional complex dynamic environment. In addition, although this type of method does not need to map the magnetic field a priori, the magnetic characteristic fingerprint of the target area must be obtained in advance as a heuristic guiding target, and the system cannot converge to a predetermined target point through searching. In the aspect of data-driven navigation, lee et al published AMID: accurate Magnetic Indoor Localization Using DEEP LEARNING in the sense journal in 2018, and the article proposed a pure data-driven positioning system named AMID, which is characterized in that by collecting a three-dimensional geomagnetic vector sequence and converting the three-dimensional geomagnetic vector sequence into recursive pattern features, and then carrying out pattern recognition and classification on fingerprint images by using a deep convolutional neural network, high-precision recognition of geomagnetic features in an indoor environment is realized, and the recognition difficulty caused by uneven magnetic field distribution in a complex indoor layout by a traditional method is remarkably solved, wherein the error of the tw