Synthetic aperture radar autofocus based on a bilinear model

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Autofocus algorithms are used to restore images in nonideal synthetic aperture radar imaging systems. In this paper, we propose a bilinear parametric model for the unknown image and the nuisance phase parameters and derive an efficient maximum-likelihood autofocus (MLA) algorithm. In the special case of a simple image model and a narrow range of look angles, MLA coincides with the successful multichannel autofocus (MCA). MLA can be interpreted as a generalization of MCA to a larger class of models with a larger range of look angles. We analyze its advantages over previous extensions of MCA in terms of identifiability conditions and noise sensitivity. As a byproduct, we also propose numerical approximations to the difficult constant modulus quadratic program that lies at the core of these algorithms. We demonstrate the superior performance of our proposed methods using computer simulations in both the correct and mismatched system models. MLA performs better than other methods, both in terms of the mean squared error and visual quality of the restored image.

Original languageAmerican English
Article number6129427
Pages (from-to)2735-2746
Number of pages12
JournalIEEE Transactions on Image Processing
Volume21
Issue number5
DOIs
StatePublished - May 2012

Keywords

  • Autofocus
  • Fourier-domain multichannel autofocus (FMCA)
  • maximum-likelihood estimation
  • multichannel autofocus (MCA)
  • phase gradient autofocus (PGA)
  • semi definite relaxation (SDR)
  • sharpness-maximization autofocus
  • spotlight-mode synthetic aperture radar (SAR)
  • successive cancellation approach (SCA)

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