Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/98537
Title: | Stable signal recovery in compressed sensing with a structured matrix perturbation | Authors: | Yang, Zai Zhang, Cishen Xie, Lihua |
Keywords: | DRNTU::Engineering::Electrical and electronic engineering | Issue Date: | 2012 | Conference: | IEEE International Conference on Acoustics, Speech and Signal Processing (2012 : Kyoto, Japan) | Abstract: | The sparse signal recovery in standard compressed sensing (CS) requires that the sensing matrix is exactly known. The CS problem subject to perturbation in the sensing matrix is often encountered in practice and has attracted interest of researches. Unlike existing robust signal recoveries with the recovery error growing linearly with the perturbation level, this paper analyzes the CS problem subject to a structured perturbation to provide conditions for stable signal recovery under measurement noise. Under mild conditions on the perturbed sensing matrix, similar to that for the standard CS, it is shown that a sparse signal can be stably recovered by ℓ1 minimization. A remarkable result is that the recovery is exact and independent of the perturbation if there is no measurement noise and the signal is sufficiently sparse. In the presence of noise, largest entries (in magnitude) of a compressible signal can be stably recovered. The result is demonstrated by a simulation example. | URI: | https://hdl.handle.net/10356/98537 http://hdl.handle.net/10220/13404 |
DOI: | 10.1109/ICASSP.2012.6288483 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2012 IEEE. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Conference Papers |
SCOPUSTM
Citations
50
6
Updated on Mar 24, 2024
Page view(s) 50
533
Updated on Mar 28, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.