DocumentCode :
2977064
Title :
Partitioned compressive sensing with neighbor-weighted decoding
Author :
Kung, H.T. ; Tarsa, Stephen J.
Author_Institution :
Harvard Univ., Cambridge, MA, USA
fYear :
2011
fDate :
7-10 Nov. 2011
Firstpage :
149
Lastpage :
156
Abstract :
Compressive sensing has gained momentum in recent years as an exciting new theory in signal processing with several useful applications. It states that signals known to have a sparse representation may be encoded and later reconstructed using a small number of measurements, approximately proportional to the signal´s sparsity rather than its size. This paper addresses a critical problem that arises when scaling compressive sensing to signals of large length: that the time required for decoding becomes prohibitively long, and that decoding is not easily parallelized. We describe a method for partitioned compressive sensing, by which we divide a large signal into smaller blocks that may be decoded in parallel. However, since this process requires a significant increase in the number of measurements needed for exact signal reconstruction, we focus on mitigating artifacts that arise due to partitioning in approximately reconstructed signals. Given an error-prone partitioned decoding, we use large magnitude components that are detected with highest accuracy to influence the decoding of neighboring blocks, and call this approach neighbor-weighted decoding. We show that, for applications with a predefined error threshold, our method can be used in conjunction with partitioned compressive sensing to improve decoding speed, requiring fewer additional measurements than unweighted or locally-weighted decoding.
Keywords :
compressed sensing; decoding; signal reconstruction; decoding speed; error-prone partitioned decoding; exact signal reconstruction; gained momentum; locally-weighted decoding; neighbor-weighted decoding; neighboring blocks; partitioned compressive sensing; scaling compressive sensing; signal processing; sparse representation; Bismuth; Compressed sensing; Decoding; Finite wordlength effects; Frequency measurement; Matching pursuit algorithms; Silicon;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
MILITARY COMMUNICATIONS CONFERENCE, 2011 - MILCOM 2011
Conference_Location :
Baltimore, MD
ISSN :
2155-7578
Print_ISBN :
978-1-4673-0079-7
Type :
conf
DOI :
10.1109/MILCOM.2011.6127519
Filename :
6127519
Link To Document :
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