Use of genetic algorithms for contrast and entropy optimization in ISAR autofocusing

Authors:
Marco Martorella;Fabrizio Berizzi;Silvia Bruscoli
Affiliations:
Department of Information Engineering, University of Pisa, Via Caruso, Pisa, Italy;Department of Information Engineering, University of Pisa, Via Caruso, Pisa, Italy;Department of Information Engineering, University of Pisa, Via Caruso, Pisa, Italy
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

Citing 4
Cited 0

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Optimization: algorithms and consistent approximations

Optimization: algorithms and consistent approximations
Translational motion compensation in ISAR image processing

IEEE Transactions on Image Processing

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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.