Model-based compressive sensing for signal ensembles
Model-based compressive sensing for signal ensembles
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DOI：
10.1109/allerton.2009.5394807
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
Marco F. Duarte;V. Cevher;Richard Baraniuk
中科院分区：
文献类型：
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作者：
Marco F. Duarte;V. Cevher;Richard Baraniuk
压缩感知（CS）是用于获取稀疏或可压缩信号的香农/奈奎斯特采样的替代方案。我们不采用N个周期性样本，而是用随机向量测量M <$N个内积，然后通过稀疏搜索优化或贪婪算法恢复信号。一个新的框架CS的基础上的工会的子空间，可以提高信号恢复，包括之间的依赖关系的值和位置的信号的重要系数。在本文中，我们将这个框架扩展到一个共同的稀疏支持模型下的信号集成的收购。新的框架提供了理论上的性能保证恢复算法。此外，该框架可以自然地扩展到大型传感器网络：每个信号所需的测量数量不会随着网络变得更大而增加。此外，恢复算法的复杂度仅与网络的大小成线性关系。我们提供了使用合成和真实信号的实验结果，证实了这些好处。
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Instead of taking N periodic samples, we measure M ≪ N inner products with random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm. A new framework for CS based on unions of subspaces can improve signal recovery by including dependencies between values and locations of the signal's significant coefficients. In this paper, we extend this framework to the acquisition of signal ensembles under a common sparse supports model. The new framework provides recovery algorithms with theoretical performance guarantees. Additionally, the framework scales naturally to large sensor networks: the number of measurements needed for each signal does not increase as the network becomes larger. Furthermore, the complexity of the recovery algorithm is only linear in the size of the network. We provide experimental results using synthetic and real-world signals that confirm these benefits.