tailieunhanh - Báo cáo hóa học: " Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 87298 Pages 1-11 DOI ASP 2006 87298 Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing Marco Martorella Fabrizio Berizzi and Silvia Bruscoli Department of Information Engineering University of Pisa Via Caruso 56126 Pisa Italy Received 4 May 2005 Revised 25 October 2005 Accepted 21 December 2005 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. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION ISAR image reconstruction has been a widely addressed topic in the last few decades 1-4 . The exploitation of large bandwidth signals and the coherent integration of the echoes provide the basis for the ISAR image formation. Before the actual image formation the signal phase must be compensated in order to remove the target radial movement. We indicate such an operation with image focusing and when no ancillary data are available with image autofocusing because only the received signal is used to perform such an operation. Among the autofocusing techniques proposed in the literature 5-12 some are based on the use of image focus indicators such as the image contrast and the image entropy 5-7 . In particular when the target radial velocity can be approximated with polynomial models the optimization problems