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

Integrated parameter estimation method for attitude and orbit of a spatial non-cooperative target based on depth learning

A non-cooperative target and parameter estimation technology, which is applied in the field of attitude-orbit integration parameter estimation of space non-cooperative targets based on deep learning, can solve problems such as over-fitting, and achieve generalization and robustness Effect

Active Publication Date: 2019-01-29
NORTHWESTERN POLYTECHNICAL UNIV
View PDF2 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Under measurement failure conditions, if these parameters are estimated using traditional neural networks, it is prone to overfitting

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
  • Integrated parameter estimation method for attitude and orbit of a spatial non-cooperative target based on depth learning
  • Integrated parameter estimation method for attitude and orbit of a spatial non-cooperative target based on depth learning
  • Integrated parameter estimation method for attitude and orbit of a spatial non-cooperative target based on depth learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] The present invention is a space non-cooperative target attitude-orbit integration parameter estimation method based on deep learning, which is a hybrid parameter estimation method. When the measurement information of the measurement sensor of the serving spacecraft about the space non-cooperative target is valid, the serving spacecraft uses the traditional extended Kalman filter algorithm to estimate the parameters of the space non-cooperative target; When the measurement information of the cooperative target fails, the service spacecraft uses a deep learning-based method for estimating the parameters of the space non-cooperative target to estimate the parameters of the space non-cooperative target. At the same time, the estimated result is used to reset the extended Kalman filter.

[0041] Specifically, the kinematics and dynamics modeling method of the space non-cooperative target considering the chance of the inertia product of the dual vector quaternion uses the du...

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 an integrated parameter estimation method for attitude and orbit of a spatial non-cooperative target based on depth learning. The parameter estimation algorithm models the kinematics and dynamics of the space non-cooperative targets by dual vector quaternion, and designs the corresponding state BP neural network parameter estimation algorithm and state covariance matrix convolution neural network parameter estimation algorithm on this basis. The attitude and orbit parameters of space non-cooperative targets are estimated integrally by using dual vector quaternion, and the attitude and orbit coupling effect of space non-cooperative targets is considered. At the same time, BP neural network with single hidden layer and convolution neural network with double hidden layers are designed to estimate the parameters of spatial non-cooperative targets under the condition of measurement failure, so that the parameter estimation algorithm has strong robustness to the spatial environment.

Description

technical field [0001] The invention belongs to the field of aerospace technology, and in particular relates to a method for estimating parameters of attitude-orbit integration of space non-cooperative targets based on deep learning. Background technique [0002] The ever-increasing amount of space debris has seriously affected normal human spaceflight activities. In particular, the growing number of failed spacecraft not only occupies a large amount of orbital resources, but also poses a great threat to space security. In recent years, major aerospace countries and international research institutions have reached a general consensus: in order to ensure the availability of orbital resources and the safety of space, it is necessary to remove space debris in space, especially failed spacecraft. During this process, due to failure, the spacecraft cannot provide its own information and rolls freely in space. Therefore, accurate parameter estimation is very important for subseq...

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): G06F17/50G06N3/04
CPCG06F2119/06G06F30/20G06N3/045
Inventor 袁建平侯翔昊张博马川孙冲崔尧
Owner NORTHWESTERN POLYTECHNICAL UNIV
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