Hard Thresholding Pursuit with Partially Known Support for Compressed Sensing

Rui Wu; Wei Huang

doi:10.4028/www.scientific.net/AMR.718-720.669

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 718-720Hard Thresholding Pursuit with Partially Known...

Hard Thresholding Pursuit with Partially Known Support for Compressed Sensing

Abstract:

Compressed sensing has attracted lots of interest in recent years. Recent works in modified compressed sensing exploited the prior information about the signal to reduce the number of measurements. In this paper, we propose a hard thresholding pursuit algorithm with partially known support (HTP-PKS), which incorporates the prior support information into the recovery process. Theoretical analysis shows that by using prior information of partially known support, the HTP-PKS algorithm presents stable and robust recovery performance under a relaxed restricted isometry property (RIP) condition. To illustrate, simulation experiments are given.

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Periodical:

Advanced Materials Research (Volumes 718-720)

Pages:

669-674

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.718-720.669

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Online since:

July 2013

Authors:

Rui Wu, Wei Huang

Keywords:

Compressed Sensing, Hard Thresholding Pursuit, Signal Reconstruction, Sparse Approximation

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