首页> 外文学位 >Design and analysis of an Euler transformation algorithm applied to full-polarimetric ISAR imagery.
【24h】

Design and analysis of an Euler transformation algorithm applied to full-polarimetric ISAR imagery.

机译:用于全极化ISAR图像的Euler变换算法的设计和分析。

获取原文
获取原文并翻译 | 示例

摘要

Use of an Inverse Synthetic Aperture Radar (ISAR) enables the construction of spatial images of an object's electromagnetic backscattering properties. A set of fully polarimetric ISAR images contains sufficient information to construct the coherent scattering matrix for each resolution cell in the image. A diagonalization of the scattering matrix is equivalent to a transformation to a common basis, which allows the extraction of phenomenological parameters. These phenomenological scattering parameters, referred to as Euler parameters, better quantify the physical scattering properties of the object than the original polarization parameters. The accuracy and meaning of the Euler parameters are shown to be degraded by transform ambiguities as well as by azimuthal nonpersistence. The transform ambiguities are shown to be removed by a case-wise characterization and redefinition of the Euler parameters. The azimuthal nonpersistence is shown to be a result of multiple scattering centers occupying the same cell.; An optimized Euler transformation algorithm is presented that removes transform ambiguities and minimizes the impact of cells containing multiple scattering centers. The accuracy of the algorithm is analyzed by testing its effectiveness in Automatic Target Recognition (ATR) using polarimetric scattering signatures obtained at the University of Massachusetts Lowell Submillimeter-Wave Technology Laboratory and the U.S. Army National Ground Intelligence Center. Finally, a complete ATR algorithm is presented and analyzed which uses the optimized Euler transformation without any previous knowledge and without human intervention. The algorithm is shown to enable successful automatic target recognition.
机译:使用逆合成孔径雷达(ISAR)可以构造物体的电磁反向散射特性的空间图像。一组完全极化的ISAR图像包含足够的信息,可为图像中的每个分辨率像元构建相干散射矩阵。散射矩阵的对角化等效于对公共基础的变换,该变换允许提取现象学参数。这些现象散射参数(称为欧拉参数)比原始偏振参数更好地量化了对象的物理散射特性。欧拉参数的准确性和含义被显示为由于变换歧义以及方位角的非持久性而降低。通过按情况描述和重新定义Euler参数,可以消除变换的歧义。方位角非持久性被证明是由于多个散射中心占据了同一细胞而引起的。提出了一种优化的欧拉变换算法,该算法消除了变换歧义,并使包含多个散射中心的细胞的影响最小化。通过使用在马萨诸塞州洛厄尔大学亚毫米波技术实验室和美国陆军国家地面情报中心获得的偏振散射特征测试自动目标识别(ATR)的有效性来分析算法的准确性。最后,提出并分析了完整的ATR算法,该算法使用优化的Euler变换,而无需任何先验知识,也无需人工干预。所示算法可以成功实现自动目标识别。

著录项

  • 作者单位

    University of Massachusetts Lowell.;

  • 授予单位 University of Massachusetts Lowell.;
  • 学科 Physics Electricity and Magnetism.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 90 p.
  • 总页数 90
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 电磁学、电动力学;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号