Maximum likelihood SAR autofocus with low-return region

Kuang Hung Liu*, Ami Wiesel, David C. Munson

*Corresponding author for this work

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

1 Scopus citations

Abstract

Autofocus algorithms deal with image restoration in a nonideal synthetic aperture radar (SAR) imaging system. We propose a novel autofocus algorithm, denoted as MLA, that is based on maximum likelihood estimation. MLA belongs to a class of autofocus algorithms that rely on a known low-return region in the underlying image. We find conditions under which MLA is equivalent to previous methods belonging to the same class. Simulation results show that when compared to previous methods, MLA performs better both in terms of visual quality of the restored image and mean square error (MSE) of the estimated unknown parameters.

Original languageAmerican English
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages1377-1380
Number of pages4
DOIs
StatePublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Fourier-domain multichannel autofocus
  • autofocus
  • maximum likelihood estimator
  • multichannel autofocus
  • synthetic aperture radar

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