SAR图像中运动目标重聚焦改进的最小熵方法
An improved method of the minimum entropy for refocusing the moving target image in the SAR observation
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摘要: 合成孔径雷达(Synthetic Aperture Radar，SAR)利用回波多普勒(Doppler)频移生成高分辨率的SAR图像。高速运动的目标产生额外的多普勒频移，在SAR图像上会呈现出带有拖曳尾迹的散焦图像。最小熵方法是一种无参数的优化方法。该文以图像的信息熵为目标函数，对最小熵方法的优化算法进行了改进，加快该方法对于目标重聚焦的实现速度。该文中用模拟的飞行目标散焦图像进行最小熵优化实现重聚焦，为在SAR图像中快速识别模糊散焦的运动目标提供了一种应用方法。Abstract: Employing the synthetic technology with the Doppler shift, the high resolution image has been achieved by the observation of the space- or air-bome SAR (Syiitlietic. Aperture R.adar). However, unexpected Doppler shift caused by the high speed moving target can defocus the image. The Minimum Entropy Method (MEM) is an optimized method with no request of additional parameters. In this paper, an improved method of MEM is developed. The entropy, as a target function, is optimized for refocusing the image. This method is demonstrated fast and effective when it is applied to a simulated SAR image of a moving aircraft.
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S.A. Hovanessian, Introduction to Synthetic Array and Imaging Radars, Mass., Artech House,1980, Chap.1. ［2］T. Itoh, H. Sueda, T. Wantanabe, Motion compensation for ISAR via centroid tracking, IEEE Trans. on Aerospace and Electronic Systems, 1996, AES-32(3), 1191-1197.[2]D.E. Wahl, P. H. Eichel, D. C. Ghiglia, C, V. Jakowatz Jr., Phase gradient auto-focusing-A robust tool for high resolution SAR phase correction, IEEE Trans. on Aerospace and Electronic Systems, 1994, AES-30(3), 827-835.[3]S. Werness, W. Carrara, L. Joyce, D. Franczak, Moving target imaging algorithm for SAR data,IEEE Trans. on Aerospace and Electronic Systems, 1990, AES-21(1), 57-67.[4]X. Li, G. S. Liu, J. L. Ni, Auto-focusing of ISAR images based on entropy minimization, IEEE Trans. on Aerospace and Electronic System, 1999, AES-35(4), 1240-1251.[5]D. Kasilingam, J. F. Wang, J. S. Lee, R. Jensen, Focusing of synthetic aperture radar images of moving targets using minimum entropy adaptive filters, IGARSS 2000, Hawaii, 74-76.[6]C. Oliver, S. Quegan, Understanding Synthetic Aperture Radar Images, Mass., Artech House,1998, Chap.1. 期刊类型引用(5)
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