Full-tensor magnetic gradient positioning detection data filtering method

A positioning detection and full tensor technology, applied in the field of magnetic measurement, can solve the problems of magnetic target recognition and positioning difficulties, achieve the effect of eliminating data noise, improving signal-to-noise ratio, and improving magnetic target detection and positioning capabilities

Active Publication Date: 2019-05-24
中船海洋探测技术研究院有限公司
View PDF12 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Using the method of magnetic field gradient tensor, by analyzing the abnormal characteristics of various magnetic bodies, the magnetic anomaly detection, precise positioning and tracking of magnetic targets under complex conditions can be realized. However, there are a lot of interference a...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Full-tensor magnetic gradient positioning detection data filtering method
  • Full-tensor magnetic gradient positioning detection data filtering method
  • Full-tensor magnetic gradient positioning detection data filtering method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0045] figure 1 It is a flow chart of a filtering method for full tensor magnetic gradient positioning detection data provided by an embodiment of the present invention, such as figure 1 As shown, the method may include the following steps:

[0046] Step 1, collect magnetic field data at fixed points, determine the magnetic field components of the magnetic field data in the three directions of x, y, and z and the first-order gradient components of each magnetic field component in the directions of x, y, and z according to the three-dimensional Cartesian coordinate system, and obtain 12 Component data, in which 9 first-order gradient components constitute the full tensor magnetic gradient data.

[0047] When filtering each component data, generally at least 10,000 data are required.

[0048] optional, combined with reference figure 2 , i...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to the field of magnetic measurement and discloses a full-tensor magnetic gradient positioning detection data filtering method. The method includes: performing fixed-point acquisition of magnetic field data, and determining magnetic field components of the magnetic field data in directions x, y and z and first-order gradient components of each magnetic field component in directions x, y and z to obtain data of 12 components; determining 5 independent components in full-tensor magnetic gradient data; aiming at data of each component, decomposing the component data into thesum of intrinsic mode functions in preset quantity according to an empirical mode decomposition algorithm; determining reconstruction parameters of the three magnetic field components and the five independent component according to an energy threshold method, and performing construction; as for data of the four components not participated in reconstruction, performing similar reconstruction by adopting same reconstruction parameters according to a data corresponding relation in full tensor; integrating reconstruction data of the 12 components to output as final filter data. By adoption of themethod, data noise can be effectively eliminated, and a single-to-noise ratio of the data is increased.

Description

technical field [0001] The invention relates to the field of magnetic measurement, in particular to a filtering method for full tensor magnetic gradient positioning detection data. Background technique [0002] The full tensor magnetic field gradient refers to the derivation of the magnetic field in three-dimensional space along three directions. There are nine gradient values ​​in total. According to the gradient value information and the size of the magnetic field, the position of the magnetic target and the size of the matrix can be calculated. [0003] Using the method of magnetic field gradient tensor, by analyzing the abnormal characteristics of various magnetic bodies, the magnetic anomaly detection, precise positioning and tracking of magnetic targets under complex conditions can be realized. However, there are a lot of interference and error information in the measurement process, and complex magnetic Background information, these interferences have brought a lot of...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01V3/08G01V3/38G01R33/022G01R33/10
Inventor 余景锋陈庆作
Owner 中船海洋探测技术研究院有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap