Heterogeneous information fusion positioning method based on deep learning

A technology of deep learning and heterogeneous information, applied in the field of radiation source positioning, can solve the problems of weak environmental adaptability and achieve the effect of good adaptability and low complexity

Active Publication Date: 2022-03-25
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF5 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

During the implementation of the technical solution of the present invention, the inventor found that the positioning method of the traditional mathematical fusion algorithm has weak environmental adaptability, and the positioning method needs to be improved

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
  • Heterogeneous information fusion positioning method based on deep learning
  • Heterogeneous information fusion positioning method based on deep learning
  • Heterogeneous information fusion positioning method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0033] An embodiment of the present invention provides a heterogeneous information fusion positioning method based on deep learning, which is used to solve the radiation source positioning problem based on heterogeneous information fusion.

[0034] see figure 2 , the heterogeneous information fusion positioning method based on deep learning provided by the embodiment of the present invention includes three parts: preprocessing, CNN (convolutional neural network) feature extraction, and radiation source position prediction. AOA, TDOA, and RSS data, as well as preprocessing corresponding to the position information of each sensing node to generate a heat map of three channels, and perform data fusion to generate input data of a specifie...

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 discloses a heterogeneous information fusion positioning method based on deep learning, and belongs to the technical field of radiation source positioning. According to the invention, based on various specified positioning parameters, fusion positioning of heterogeneous information based on deep learning is realized. Positioning parameters are converted into a thermodynamic diagram, radiation source position estimation is regarded as a key point detection problem in the thermodynamic diagram, end-to-end training and testing are directly carried out, and the implementation complexity is low. Compared with a traditional fusion positioning method, different parameter information at different sensing nodes can be fused, the assumed condition that all the distributed sensing nodes can obtain multiple parameters at the same time is not needed, and the actual dynamic environment requirement can be better met. According to the invention, during actual positioning processing, the same network model can be used regardless of one, two or three of RSS, AOA or TDOA parameters, and the adaptability is good. Meanwhile, the method can be expanded to fusion positioning containing more heterogeneous information such as TOA, FDOA and other parameters.

Description

technical field [0001] The invention belongs to the technical field of radiation source positioning, and in particular relates to a heterogeneous information fusion positioning method based on deep learning. Background technique [0002] With the improvement of passive positioning accuracy requirements in practical applications and the development of measurement equipment and technology, a variety of heterogeneous information fusion is gradually applied in passive positioning, mainly including AOA (angle of arrival) and TDOA (time difference of arrival) , RSS (Received Signal Strength), FDOA (Frequency Difference of Arrival) and other heterogeneous parameter information. Radiation source location scenarios based on heterogeneous information fusion are as follows: figure 1 As shown, suppose there is a radiation source in the rectangular area of ​​two-dimensional space, its position (s x ,s y ) to be estimated. M sensing nodes are deployed at known locations in the area, w...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/80G06V10/778G06K9/62G06N3/04G06N3/08G01S5/02
CPCG06N3/08G01S5/02685G06N3/045G06F18/217G06F18/25Y02D30/70
Inventor 韩欢彭启航张阿芳王军
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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