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Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 087298 (2006) Cite this article

Abstract

Image contrast maximization and entropy minimization are two commonly used techniques for ISAR image autofocusing. When the signal phase history due to the target radial motion has to be approximated with high order polynomial models, classic optimization techniques fail when attempting to either maximize the image contrast or minimize the image entropy. In this paper a solution of this problem is proposed by using genetic algorithms. The performances of the new algorithms that make use of genetic algorithms overcome the problem with previous implementations based on deterministic approaches. Tests on real data of airplanes and ships confirm the insight.

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Authors and Affiliations

  1. Department of Information Engineering, University of Pisa, Via Caruso, Pisa, 56126, Italy

    Marco Martorella, Fabrizio Berizzi & Silvia Bruscoli

Authors
  1. Marco Martorella
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  2. Fabrizio Berizzi
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  3. Silvia Bruscoli
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Corresponding author

Correspondence to Marco Martorella.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Martorella, M., Berizzi, F. & Bruscoli, S. Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing. EURASIP J. Adv. Signal Process. 2006, 087298 (2006). https://doi.org/10.1155/ASP/2006/87298

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  • DOI: https://doi.org/10.1155/ASP/2006/87298

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