期刊文献+

低空目标的SAR成像特性及阴影分析

Analysis on SAR Imaging Characteristics of Targets at Low Altitude
下载PDF
导出
摘要 低空目标和地表目标的成像不尽相同,针对低空目标SAR成像的研究微乎其微。为弥补此欠缺,在推导出低空目标的SAR成像输出后,从有效成像区域、阴影效应以及多航过成像三个方面分析了低空目标的成像特性,确定了SAR的有效成像区域,给出了十种典型情况下利用低空目标阴影信息提取目标高度的公式,并指出了多航过飞行对低空目标成像的影响。实例验证了高度提取方法的有效性。该工作为SAR目标的成像特性研究开辟了新思路。 There are some differences between SAR imagery for targets at low altitude and targets on the ground surface. Little Studies relates to the SAR imagery for targets at low altitude. To make up for this lack, after the derivation of SAR imaging output for targets at low altitude, SAR imaging characteristics are analyzed from the three aspects of effective imaging area, shadow effect and multi- pass imaging. The effective imaging area is determined, the height extraction formulas in ten typical situations are presented using shadow information, and the influence of multi-pass flights on imaging is analyzed. An actual example proves the validity of the presented formula. This research breaks a new path for SAR target imaging characteristic study.
出处 《电子对抗》 2012年第1期21-25,30,共6页 Electronic Warfare
关键词 合成孔径雷达 低空目标 成像区域 阴影 高度 Synthetic Aperture Radar (SAR) targets at low altitude imaging area shadow height
  • 相关文献

参考文献10

二级参考文献38

  • 1付琨,匡纲要,郁文贤.高分辨率SAR图像地物分类算法研究[J].电子学报,2001,29(z1):1820-1823. 被引量:6
  • 2钱俊,舒宁,詹总谦.基于区域增长的单幅雷达测图算法[J].武汉大学学报(信息科学版),2004,29(7):624-627. 被引量:1
  • 3[2]Yoshihisa Hara, Robert G Atkins, et al. Application of neural networks to radar image classification [ J ]. IEEE Trans on Geoscience and Remote Sensing, 1994,32( 1 ): 100 - 109.
  • 4[5]Poggio T,F Grossi. Networks for approximation and learning[J].Proceedings of the IEEE, 1998,78(9):1481 - 1497.
  • 5[6]HWANG YS,BANGSY.An efficient method to construct a radial basis function neural network classifier [ J ]. Neural Networks, 1997, 10(8): 1495 - 1503.
  • 6[7]Girosi F. Some extensions of radial basis functions and their applications in artificial intelligence[ J ]. Computers Math, 1992,24 (12): 61 -80.
  • 7[8]Rinivasa V C,Joydeep Ghosh. Scale-based Clustering using the Radial Basis Function Network [ EB/OL ]. http://pegasus. ece. utexas. edu/journals. html.
  • 8[9]B Fritzke. Incremental neuro-fuzzy systems[ A ]. Proc SPIE's Optical Science, Engineering and Instrumentation' 97: Applications of Fuzzy Logic Technology Ⅳ[ C] .San Diego, CA:SPIE Press, 1997.2- 10.
  • 9[10]B Fritzke. Fast Learning with Incremental RBF Networks[J]. Neural Processing Letters, 1994,1 (1) :2 - 5.
  • 10边肇祺 张学工.模式识别(第二版)[M].北京:清华大学出版社,1999.12.

共引文献32

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部