Sparse signal recovery in Hilbert spaces

Authors

Graeme Pope and Helmut Bölcskei

Reference

Proc. of IEEE International Symposium on Information Theory (ISIT), Boston, MA, pp. 1463-1467, July 2012.

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Abstract

This paper reports an effort to consolidate numerous coherence-based sparse signal recovery results available in the literature. We present a single theory that applies to general Hilbert spaces with the sparsity of a signal defined as the number of (possibly infinite-dimensional) subspaces participating in the signal’s representation. Our general results recover uncertainty relations and coherence-based recovery thresholds for sparse signals, block-sparse signals, multi-band signals, signals in shift-invariant spaces, and signals in finite unions of (possibly infinite-dimensional) subspaces. Moreover, we improve upon and generalize several of the existing results and, in many cases, we find shortened and simplified proofs.


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