• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Information Theory >An Information-Theoretic Study for Joint Sparsity Pattern Recovery With Different Sensing Matrices
【24h】

An Information-Theoretic Study for Joint Sparsity Pattern Recovery With Different Sensing Matrices

机译：不同传感矩阵的联合稀疏模式恢复的信息理论研究

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

In this paper, we study a support set reconstruction problem for multiple measurement vectors (MMV) with different sensing matrices, where the signals of interest are assumed to be jointly sparse and each signal is sampled by its own sensing matrix in the presence of noise. Using mathematical tools, we develop upper and lower bounds of the failure probability of the support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal-to-noise ratio, the number of measurement vectors, and the number of measurements. These bounds can be used to provide guidelines for determining the system parameters for various compressed sensing applications with noisy MMV with different sensing matrices. Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. We interpret these conditions to provide theoretical explanations regarding the benefits of taking more measurement vectors. We then compare our sufficient condition with the existing results for noisy MMV with the same sensing matrix. As a result, we show that noisy MMV with different sensing matrices may require fewer measurements for reliable support set reconstruction, under a sublinear sparsity regime in a low noise-level scenario.
机译：在本文中，我们研究了具有不同传感矩阵的多个测量向量（MMV）的支持集重构问题，其中假设感兴趣的信号被共同稀疏，并且每个信号在存在噪声的情况下都由其自己的传感矩阵进行采样。使用数学工具，我们根据稀疏性，环境维度，最小信噪比，测量向量的数量和测量的数量，开发了支持集重建失败概率的上限和下限。这些界限可用于为使用带有不同传感矩阵的MMV噪声的各种压缩传感应用程序确定系统参数提供指导。基于边界，我们为可靠的支持集重建开发了必要和充分的条件。我们解释这些条件以提供有关采用更多测量向量的好处的理论解释。然后，我们将充足条件与具有相同传感矩阵的MMM噪声的现有结果进行比较。结果，我们表明，在低噪声级别的情况下，在亚线性稀疏状态下，具有不同传感矩阵的嘈杂MMV可能需要较少的测量来进行可靠的支持集重构。

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