DocumentCode
57832
Title
Adaptive Compressed Sensing via Minimizing Cramer–Rao Bound
Author
Tianyao Huang ; Yimin Liu ; Huadong Meng ; Xiqin Wang
Author_Institution
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
Volume
21
Issue
3
fYear
2014
fDate
Mar-14
Firstpage
270
Lastpage
274
Abstract
This letter considers the problem of observation strategy design for compressed sensing. An adaptive method, based on Cramer-Rao bound minimization, is proposed to design the sensing matrix. Simulation results demonstrate that the adaptively constructed sensing matrix can lead to much lower recovery errors than those of traditional Gaussian matrices and some existing adaptive approaches.
Keywords
adaptive signal processing; compressed sensing; estimation theory; matrix algebra; minimisation; Cramer-Rao bound minimization; Gaussian matrices; adaptive compressed sensing; adaptively constructed sensing matrix; recovery errors; Compressed sensing; Cramer-Rao bounds; Sensors; Signal processing algorithms; Signal to noise ratio; Sparse matrices; Vectors; Adaptive sampling; Cramer–Rao bound; compressed sensing; subspace pursuit;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/LSP.2014.2299814
Filename
6710151
Link To Document