Frame coherence and sparse signal processing

Published

Journal Article

The sparse signal processing literature often uses random sensing matrices to obtain performance guarantees. Unfortunately, in the real world, sensing matrices do not always come from random processes. It is therefore desirable to evaluate whether an arbitrary matrix, or frame, is suitable for sensing sparse signals. To this end, the present paper investigates two parameters that measure the coherence of a frame: worst-case and average coherence. We first provide several examples of frames that have small spectral norm, worst-case coherence, and average coherence. Next, we present a new lower bound on worst-case coherence and compare it to the Welch bound. Later, we propose an algorithm that decreases the average coherence of a frame without changing its spectral norm or worst-case coherence. Finally, we use worst-case and average coherence, as opposed to the Restricted Isometry Property, to garner near-optimal probabilistic guarantees on both sparse signal detection and reconstruction in the presence of noise. This contrasts with recent results that only guarantee noiseless signal recovery from arbitrary frames, and which further assume independence across the nonzero entries of the signalin a sense, requiring small average coherence replaces the need for such an assumption. © 2011 IEEE.

Full Text

Duke Authors

Cited Authors

  • Mixon, DG; Bajwa, WU; Calderbank, R

Published Date

  • October 26, 2011

Published In

  • Ieee International Symposium on Information Theory Proceedings

Start / End Page

  • 663 - 667

Digital Object Identifier (DOI)

  • 10.1109/ISIT.2011.6034214

Citation Source

  • Scopus