Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Magnetic anomaly multi-feature information extraction method based on Hilbert-Huang transform

An information extraction and multi-feature technology, applied in complex mathematical operations, instruments, character and pattern recognition, etc., can solve the problems of reduced detection probability and increased false alarm probability of target detection, so as to improve detection performance and weak magnetic anomaly detection Ability, performance-enhancing effects

Inactive Publication Date: 2021-04-13
西北工业大学青岛研究院 +1
View PDF3 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This increases the false alarm probability of target detection and reduces the detection probability

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
  • Magnetic anomaly multi-feature information extraction method based on Hilbert-Huang transform
  • Magnetic anomaly multi-feature information extraction method based on Hilbert-Huang transform
  • Magnetic anomaly multi-feature information extraction method based on Hilbert-Huang transform

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment

[0060] 1. Collection of magnetic field information

[0061] Construct a magnetic field gradient measurement array. The distance between sensors in the array is B=1m, and the sensitivity of the magnetic sensor is The CS-L optically pumped magnetometer. The magnetic target moves in the horizontal plane, and its magnetic moment is (11.87, 20.99, -6.56) A·m2. The target moves from the starting position (-100, 26, 1) m to the end position (100, 26, 1) m in a uniform motion.

[0062] 2. The Hilbert-Huang change of the magnetic field information to obtain the Hilbert spectrum of the signal

[0063] First, the empirical mode decomposition is used to adaptively decompose the collected magnetic field signals to obtain multiple intrinsic mode function items and a trend item; then, each intrinsic mode function is Hilbert transformed to obtain the corresponding instantaneous amplitude and instantaneous frequency.

[0064] 3. Obtain the Hilbert Pu of the magnetic signal

[0065] Ac...

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 a magnetic anomaly multi-feature information extraction method based on Hilbert-Huang transform. Non-linear and non-stationary magnetic anomaly signals are processed through Hilbert-Huang Transform (HHT), magnetic anomaly multi-feature information based on instantaneous energy density and marginal spectrum is extracted by conducting empirical mode decomposition and Hilbert spectrum analysis on the signals, and effective feature information selection is provided for magnetic anomaly detection based on a machine learning framework. According to the invention, the multi-feature information of the magnetic anomaly signal in the time-frequency domain can be obtained through the Hilbert-Huang transform processing of the signal, and the feature information selection in the magnetic anomaly detection is enriched. In the magnetic anomaly detection method based on the machine learning framework, the time-frequency domain multi-feature information of the magnetic anomaly signal is helpful to improve the detection capability of a magnetic target under a low signal-to-noise ratio, and the false alarm probability of target detection is reduced.

Description

technical field [0001] The invention relates to a method for extracting magnetic anomaly multi-feature information based on Hilbert-Huang transformation. The magnetic anomaly multi-feature information extracted by this method can provide a basis for improving the detection ability of the magnetic anomaly detection method based on the machine learning framework. The method can be widely used in the fields of energy mineral deposit survey, various underwater pipeline detection, underwater target detection, underwater archeology, shipwreck survey, mine sweeping and anti-submarine, and the like. Background technique [0002] The document "Magnetic anomaly detection based on full connected neural network [J]. IEEE Access, 2019, 7, 182198–182206" discloses a magnetic anomaly detection method based on a fully connected neural network. In this method, the fully connected neural network is used as a classification model for magnetic anomaly detection, and the orthogonal basis functi...

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): G06K9/00G06F17/18G06N20/00
CPCG06F17/18G06N20/00G06F2218/08
Inventor 樊黎明王惠刚赵维娜胡浩刘建国孙伟涛
Owner 西北工业大学青岛研究院
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com