An effective ISAR autofocus algorithm based on single eigenvector

Jin Jian Cai, Jia Xu, Guan Wang, Xiang Gen Xia, Teng Long, Ming Ming Bian

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

6 Citations (Scopus)

Abstract

Phase autofocus is a key step of translational motion compensation (TMC) in inverse synthetic aperture radar (ISAR). In this paper, an effective phase autofocus algorithm for ISAR is proposed based on single eigenvector. Samplings in multiple range bins are used to construct the covariance matrix and obtain the eigenvector corresponding to the largest eigenvalue. It is found that this eigenvector can act as the phase compensation vector for ISAR autofocus. Compared to the conventional methods, the proposed algorithm can obtain a better focused image especially in a low SNR case, as long as there is a relatively prominent scatterer in the target. Finally, experimental results based on real measured data are provided to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2016 CIE International Conference on Radar, RADAR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509048281
DOIs
Publication statusPublished - 4 Oct 2017
Event2016 CIE International Conference on Radar, RADAR 2016 - Guangzhou, China
Duration: 10 Oct 201613 Oct 2016

Publication series

Name2016 CIE International Conference on Radar, RADAR 2016

Conference

Conference2016 CIE International Conference on Radar, RADAR 2016
Country/TerritoryChina
CityGuangzhou
Period10/10/1613/10/16

Keywords

  • Inverse synthetic aperture radar (ISAR)
  • Low SNR
  • Phase autofocus
  • Single eigenvector
  • Translational motion compensation (TMC)

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