Anti-measurement Matrix Uncertainty Sparse Signal Recovery for Compressive Sensing
Abstract
Compressive sensing (CS) is a technique for estimating a sparse signal from the random measurements and the measurement matrix. Traditional sparse signal recovery methods have seriously degeneration with the measurement matrix uncertainty (MMU). Here the MMU is modeled as a bounded additive error. An anti-uncertainty constraint in the form of a mixed L2 and L1 norm is deduced from the sparse signal model with MMU. Then we combine the sparse constraint with the anti-uncertainty constraint to get an anti-uncertainty sparse signal recovery operator. Numerical simulations demonstrate that the proposed operator has a better reconstructing performance with the MMU than traditional methods.
Keywords
Cite
@article{arxiv.1006.0054,
title = {Anti-measurement Matrix Uncertainty Sparse Signal Recovery for Compressive Sensing},
author = {Yipeng Liu and Qun Wan and Fei Wen and Jia Xu and Yingning Peng},
journal= {arXiv preprint arXiv:1006.0054},
year = {2011}
}
Comments
13 pages, 3 figures; Accepted by International Journal of the Physical Sciences