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 :
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