Adaptive sensing for sparse signal recovery

Jarvis Haupt, Robert Nowak, Rui Castro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Scopus citations

Abstract

The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recovered from a relatively small number of samples in the form of random projections. However, in severely resource-constrained settings even CS techniques may fail, and thus, a less aggressive goal of partial signal recovery is reasonable. This paper describes a simple data-adaptive procedure that efficiently utilizes information from previous observations to focus subsequent measurements into subspaces that are increasingly likely to contain true signal components. The procedure is analyzed in a simple setting, and more generally, shown experimentally to be more effective than methods based on traditional (non-adaptive) random projections for partial signal recovery.

Original languageEnglish (US)
Title of host publication2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
Pages702-707
Number of pages6
DOIs
StatePublished - Apr 8 2009
Event2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009 - Marco Island, FL, United States
Duration: Jan 4 2009Jan 7 2009

Publication series

Name2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings

Other

Other2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009
CountryUnited States
CityMarco Island, FL
Period1/4/091/7/09

Keywords

  • Adaptive sampling
  • Compressed sensing
  • Signal detection and estimation
  • Sparsity

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