• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied optics >Two-beam-coupling correlator for synthetic aperture radar image recognition with power-law scattering centers preenhancement
【24h】

Two-beam-coupling correlator for synthetic aperture radar image recognition with power-law scattering centers preenhancement

机译：两束耦合相关器，用于具有幂律散射中心预增强的合成孔径雷达图像识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Synthetic radar image recognition is an area of interest for military applications including automatic target recognition, air traffic control, and remote sensing. Here a dynamic range compression two-beam-coupling joint transform correlator for detecting synthetic aperture radar targets is utilized. The joint input image consists of a prepower-law, enhanced scattering center of the input image and a linearly synthesized power-law-enhanced scattering center template. Enhancing the scattering center of both the synthetic template and the input image furnishes the conditions for achieving dynamic range compression correlation in two-beam coupling. Dynamic range compression (a) enhances the signal-to-noise ratio, (b) enhances the high frequencies relative to low frequencies, and (c) converts the noise to high frequency components. This improves the correlation-peak intensity to the mean of the surrounding noise significantly. Dynamic range compression correlation has already been demonstrated to outperform many optimal correlation filters in detecting signals in severe noise environments. The performance is evaluated via established metrics such as peak-to-correlation energy, Horner efficiency, and correlation-peak intensity. The results showed significant improvement as the power increased.
机译：合成雷达图像识别是军事应用感兴趣的领域，包括自动目标识别，空中交通管制和遥感。这里利用了用于检测合成孔径雷达目标的动态范围压缩两光束耦合联合变换相关器。联合输入图像由输入前幂定律，输入图像的增强散射中心和线性合成的幂定律增强散射中心模板组成。增强合成模板和输入图像的散射中心为在双光束耦合中实现动态范围压缩相关性提供了条件。动态范围压缩（a）增强了信噪比，（b）增强了相对于低频的高频，并且（c）将噪声转换为高频分量。这将相关峰强度显着提高到周围噪声的平均值。在严重噪声环境中检测信号时，动态范围压缩相关已被证明优于许多最佳相关滤波器。通过建立的度量标准来评估性能，例如峰与相关能量，霍纳效率和相关峰强度。随着功率的增加，结果显示出明显的改善。

著录项

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