Synthetic aperture radar autofocus via semidefinite relaxation

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

The autofocus problem in synthetic aperture radar imaging amounts to estimating unknown phase errors caused by unknown platform or target motion. At the heart of three state-of-the-art autofocus algorithms, namely, phase gradient autofocus, multichannel autofocus (MCA), and Fourier-domain multichannel autofocus (FMCA), is the solution of a constant modulus quadratic program (CMQP). Currently, these algorithms solve a CMQP by using an eigenvalue relaxation approach. We propose an alternative relaxation approach based on semidefinite programming, which has recently attracted considerable attention in other signal processing problems. Experimental results show that our proposed methods provide promising performance improvements for MCA and FMCA through an increase in computational complexity.

Original languageAmerican English
Article number6471225
Pages (from-to)2317-2326
Number of pages10
JournalIEEE Transactions on Image Processing
Volume22
Issue number6
DOIs
StatePublished - 2013

Keywords

  • Autofocus
  • Fourier-domain multichannel autofocus (FMCA)
  • multichannel autofocus (MCA)
  • semidefinite relaxation
  • synthetic aperture radar (SAR)

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