DocumentCode :
3352475
Title :
Exploiting Prior Knowledge in the Recovery of Non-Sparse Signals from Noisy Random Projections
Author :
Esnaola, Inaki ; Garcia-Frias, Javier
Author_Institution :
Delaware Univ., Newark
fYear :
2007
fDate :
14-16 March 2007
Firstpage :
731
Lastpage :
731
Abstract :
This paper illustrates that exploiting the source statistics in the recovery process results in significant performance gains, even if the signal is reconstructed in a basis in which it does not admit a sparse representation. Successful recovery will depend on the capability of exploiting all available a priori information in the basis where reconstruction is performed. The proposed framework is similar to joint source-channel coding schemes in digital communications, but applies on the analog domain.
Keywords :
random noise; signal reconstruction; statistical analysis; analog domain; compressive sensing; joint source-channel coding schemes; noisy random projections; nonsparse signals recovery; signal reconstruction; sparse representation; Compressed sensing; Digital communication; Hidden Markov models; Linear approximation; Performance gain; Signal processing; State estimation; Statistics; Stochastic processes; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Sciences and Systems, 2007. CISS '07. 41st Annual Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
1-4244-1063-3
Electronic_ISBN :
1-4244-1037-1
Type :
conf
DOI :
10.1109/CISS.2007.4298402
Filename :
4298402
Link To Document :
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