Sampling of multiple signals with finite rate of innovation and sparse common support
Sampling of multiple signals with finite rate of innovation and sparse common support
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DOI：
10.1049/iet-spr.2012.0397
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发表时间：
2014-01
期刊：
影响因子：
--
通讯作者：
Zelong Wang;Jubo Zhu
中科院分区：
文献类型：
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作者：
Zelong Wang;Jubo Zhu
研究了在有限新息率和稀疏公共支持度下多信号采样的最小采样率和精确恢复条件。首先提出了基于子空间的恢复方法，分析了该方法与零化滤波器的关系，然后将该方法应用于FRI和SCS的多信号采样。据观察，精确恢复的最小采样率在很大程度上取决于所定义的特征矩阵所描述的信号结构，基于此的充分必要条件也被提出。数值仿真结果表明，所提出的恢复方法和恢复条件对于FRI和SCS的多信号采样是可行的。
The authors focus on the minimum sampling rate and the exact recovery condition in the sampling of multiple signals with finite rate of innovation (FRI) and sparse common support (SCS). The authors first propose the subspace-based recovery method and analyse its relation with the annihilating filter; then the proposed method is used for sampling the multiple signals with FRI and SCS. It is observed that the minimum sampling rate for the exact recovery heavily depends on the signal structure described by the defined characteristic matrix, based on which a sufficient and necessary condition is also presented. The numerical simulations show that the proposed recovery method and the recovery condition are feasible for the sampling of multiple signals with FRI and SCS.