Attitude estimation of space targets by extracting line features from ISAR image sequences

Yejian Zhou, Lei Zhang, Hongxian Wang, Zhijun Qiao, Mengqi Hu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

15 Citations (Scopus)

Abstract

In this letter, an attitude estimation method is presented for space targets by using an inverse synthetic aperture radar (ISAR) image sequence. The line structures, like the boundaries of planar payloads, are extracted from the ISAR image sequence and associated from frame to frame. With the accommodation of the radar looking angle information from the trajectory, the threedimensional attitude of the target is achieved by solving an object-to-Range-Doppler (RD) projection optimization. Detailed image processing is provided for line structure extraction, orientation estimation and association. Experiment confirms the effectiveness of the proposal.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538631409
DOIs
Publication statusPublished - 29 Dec 2017
Externally publishedYes
Event7th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017 - Xiamen, Fujian, China
Duration: 22 Oct 201725 Oct 2017

Publication series

Name2017 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017
Volume2017-January

Conference

Conference7th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017
Country/TerritoryChina
CityXiamen, Fujian
Period22/10/1725/10/17

Keywords

  • attitude estimation
  • Geometrical projection matrix
  • Inverse Synthetic Aperture Radar (ISAR) imaging
  • Radon transform

Fingerprint

Dive into the research topics of 'Attitude estimation of space targets by extracting line features from ISAR image sequences'. Together they form a unique fingerprint.

Cite this