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.
Highlights
ISAR image reconstruction has been a widely addressed topic in the last few decades [1,2,3,4]
Among the autofocusing techniques proposed in the literature [5,6,7,8,9,10,11,12], some are based on the use of image focus indicators, such as the image contrast and the image entropy [5,6,7]
In this paper an extension of both the image contrast technique (ICT) and image entropy technique (IET) is proposed by introducing genetic algorithms
Summary
ISAR image reconstruction has been a widely addressed topic in the last few decades [1,2,3,4]. 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. When the target radial velocity can be approximated with polynomial models, the optimization problems that have to be solved are reduced to a search on a domain of few parameters. In these cases the computational cost is strongly reduced and real-time applications are achievable. R0(t) z1 z2 ξ30(t) ξ20(t) x2 Figure 1: Reference system
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