首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Parametric dictionary learning for sparsity-based TWRI in multipath environments
【24h】

Parametric dictionary learning for sparsity-based TWRI in multipath environments

机译:多路径环境中基于稀疏的TWRI的参数字典学习

获取原文
获取原文并翻译 | 示例
           

摘要

Sparsity-based multipath exploitation is a promising method to eliminate ghost targets in through-the-wall radar images and utilize the additional energy in secondary reflections. The applicability of existing methods, however, is limited due to the assumption of perfectly known geometry of building interiors. We develop a parametrized multipath signal model that captures unknown or partially known wall locations. This model is used in the proposed joint image reconstruction and wall position estimation method. In order to further improve practicability in realistic scenarios, a reconstruction method based on deployment of multiple small aperture radar modules is discussed. To this end, we analyze theoretical performance bounds for colocated and distributed placements of the various modules. Supporting results based on simulated and experimental lab data are provided.
机译:基于稀疏性的多路径开发是一种有前途的方法,可以消除穿墙雷达图像中的幻影目标,并在二次反射中利用额外的能量。但是,由于假定了完全已知的建筑内部几何形状,因此现有方法的适用性受到限制。我们开发了参数化的多径信号模型,可捕获未知或部分已知的壁位置。该模型用于提出的联合图像重建和墙体位置估计方法。为了进一步提高实际情况的实用性,讨论了一种基于部署多个小孔径雷达模块的重建方法。为此,我们分析了各种模块的共置和分布式布局的理论性能界限。提供了基于模拟和实验实验室数据的支持结果。

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