A Robust Sparse Signal Recovery Method for Perturbed Compressed Sensing Based on Max-min Residual Regularization

DOI

10.2991/eeic-13.2013.46How to use a DOI?

Keywords

compressed sensing; max-min; matrix uncertienties; reconstrunction algoritm; analog to information converter(AIC);

Abstract

Compressive sensing (CS) is a new signal acquisition framework for sparse and compressible signals with a sampling rate much below the Nyquist rate. In this work, we consider the problem of perturbed compressive sensing (CS) with uncertainty in the measurement matrix as well as in the measurements. In order to eliminate the effects of measurement matrix uncertainty, this paper proposed a robust reconstruction method based on max-min residual regularization. We also deduced the solver of the optimization model with the sub-gradient algorithm. Simulation and numerical results shown that the proposed recovery method performs better than the traditional reconstruction methods.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Rongzong Kang
AU  - Pengwu Tian
AU  - Hongyi Yu
PY  - 2013/12
DA  - 2013/12
TI  - A Robust Sparse Signal Recovery Method for Perturbed Compressed Sensing Based on Max-min Residual Regularization
BT  - Proceedings of the 3rd International Conference on Electric and Electronics
PB  - Atlantis Press
SP  - 199
EP  - 202
SN  - 1951-6851
UR  - https://doi.org/10.2991/eeic-13.2013.46
DO  - 10.2991/eeic-13.2013.46
ID  - Kang2013/12
ER  -