Adaptive Compressed Sensing via Minimizing Cramer-Rao Bound

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...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 21; no. 3; pp. 270 - 274
Main Authors Huang, Tianyao, Liu, Yimin, Meng, Huadong, Wang, Xiqin
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive sampling
Compressed
Compressed sensing
Cramer-Rao bound
Cramer-Rao bounds
Detection
Error detection
Gaussian
Optimization
Sensors
Signal processing algorithms
Signal to noise ratio
Simulation
Sparse matrices
Strategy
subspace pursuit
Vectors
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2299814