Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85099
Title: Greedy Pursuits Assisted Basis Pursuit for reconstruction of joint-sparse signals
Authors: Narayanan, Sathiya
Sahoo, Sujit Kumar
Makur, Anamitra
Keywords: Multiple measurement vectors
Modified basis pursuit
Issue Date: 2017
Source: Narayanan, S., Sahoo, S. K., & Makur, A. (2018). Greedy Pursuits Assisted Basis Pursuit for reconstruction of joint-sparse signals. Signal Processing, 142, 485-491.
Series/Report no.: Signal Processing
Abstract: Distributed Compressive Sensing (DCS) is an extension of compressive sensing from single measurement vector problem to Multiple Measurement Vectors (MMV) problem. In DCS, several reconstruction algorithms have been proposed to reconstruct the joint-sparse signal ensemble. However, most of them are designed for signal ensemble sharing common support. Since the assumption of common sparsity pattern is very restrictive, we are more interested in signal ensemble containing both common and innovation components. With a goal of proposing an MMV-type algorithm that is robust to outliers (absence of common sparsity pattern), we propose Greedy Pursuits Assisted Basis Pursuit for Multiple Measurement Vectors (GPABP-MMV). It employs modified basis pursuit and MMV versions of multiple greedy pursuits. We also formulate the exact reconstruction conditions and the reconstruction error bound for GPABP-MMV. GPABP-MMV is suitable for a variety of applications including time-sequence reconstruction of video frames, reconstruction of ECG signals, etc.
URI: https://hdl.handle.net/10356/85099
http://hdl.handle.net/10220/43620
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2017.08.007
Rights: © 2017 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Signal Processing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.sigpro.2017.08.007].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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