Dictionary Design for Distributed Compressive Sensing
Dictionary Design for Distributed Compressive Sensing
复制标题
DOI：
10.1109/lsp.2014.2350024
复制
发表时间：
2015
影响因子：
3.9
通讯作者：
Wei Chen;I. Wassell;M. Rodrigues
中科院分区：
文献类型：
--
作者：
Wei Chen;I. Wassell;M. Rodrigues
传统的字典学习框架试图找到一组原子，促进信号表示和信号稀疏性的一类信号。在分布式压缩感知（DCS）中，除了利用信号内相关性外，还利用信号间相关性进行联合信号重构，这超出了传统字典学习框架的目的。在这封信中，我们提出了一个新的字典学习框架，以提高DCS应用程序中的信号重建性能。通过利用捕获信号内和信号间相关性的稀疏公共分量和新息（SCCI）模型，所提出的方法迭代地找到促进各种目标的字典设计：i）信号表示; ii）信号内相关性;以及iii）信号间相关性。仿真结果表明，我们的字典设计导致了一个改进的DCS重建性能相比，其他设计。
Conventional dictionary learning frameworks attempt to find a set of atoms that promote both signal representation and signal sparsity fora class of signals. In distributed compressive sensing (DCS), in addition to intra-signal correlation, inter-signal correlation is also exploited in the joint signal reconstruction, which goes beyond the aim of the conventional dictionary learning framework. In this letter, we propose a new dictionary learning framework in order to improve signal reconstruction performance in DCS applications. By capitalizing on the sparse common component and innovations (SCCI) model , which captures both intra- and inter-signal correlation, the proposed method iteratively finds a dictionary design that promotes various goals: i) signal representation; ii) intra-signal correlation; and iii) inter-signal correlation. Simulation results showthat our dictionary design leads to an improved DCS reconstruction performance in comparison to other designs.