relative attitude estimation for a uniform motion and slowly rotating noncooperative spacecraft

Clicks: 273
ID: 142327
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #18 of 1,093 articles by views in journal of power sources

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,093 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
This paper presents a novel relative attitude estimation approach for a uniform motion and slowly rotating noncooperative spacecraft. It is assumed that the uniform motion and slowly rotating noncooperative chief spacecraft is in failure or out of control and there is no a priori rotation rate information. We utilize a very fast binary descriptor based on binary robust independent elementary features (BRIEF) to obtain the features of the target, which are rotational invariance and resistance to noise. And then, we propose a novel combination of single candidate random sample consensus (RANSAC) with extended Kalman filter (EKF) that makes use of the available prior probabilistic information from the EKF in the RANSAC model hypothesis stage. The advantage of this combination obviously reduces the sample size to only one, which results in large computational savings without the loss of accuracy. Experimental results from real image sequence of a real model target show that the relative angular error is about 3.5% and the mean angular velocity error is about 0.1 deg/s.
Reference Key
zhang2017mathematicalrelative Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Liu Zhang;Zongming Liu;Dong Ye;Guanyu Zhang
Journal journal of power sources
Year 2017
DOI
10.1155/2017/1787530
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.