Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100144
Title: Robustly stable signal recovery in compressed sensing with structured matrix perturbation
Authors: Yang, Zai
Zhang, Cishen
Xie, Lihua
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Yang, Z., Zhang, C., & Xie, L. (2012). Robustly Stable Signal Recovery in Compressed Sensing With Structured Matrix Perturbation. IEEE Transactions on Signal Processing, 60(9), 4658 - 4671.
Series/Report no.: IEEE transactions on signal processing
Abstract: The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing matrix be known a priori. Such an ideal assumption may not be met in practical applications where various errors and fluctuations exist in the sensing instruments. This paper considers the problem of compressed sensing subject to a structured perturbation in the sensing matrix. Under mild conditions, it is shown that a sparse signal can be recovered by l1 minimization and the recovery error is at most proportional to the measurement noise level, which is similar to the standard CS result. In the special noise free case, the recovery is exact provided that the signal is sufficiently sparse with respect to the perturbation level. The formulated structured sensing matrix perturbation is applicable to the direction of arrival estimation problem, so has practical relevance. Algorithms are proposed to implement the l1 minimization problem and numerical simulations are carried out to verify the results obtained.
URI: https://hdl.handle.net/10356/100144
http://hdl.handle.net/10220/13582
DOI: http://dx.doi.org/10.1109/TSP.2012.2201152
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.