NDLI: Measurement combining and progressive reconstruction in compressive sensing

Content Provider	IEEE Xplore Digital Library
Author	Hsieh-Chung Chen Kung, H.T. Vlah, D. Suter, B.
Copyright Year	2011
Description	Author affiliation: Air Force Research Laboratory, Rome, NY, USA (Suter, B.) \|\| Harvard University, Cambridge, MA, USA (Hsieh-Chung Chen; Kung, H.T.; Vlah, D.)
Abstract	Compressive sensing has emerged as an important new technique in signal acquisition due to the surprising property that a sparse signal can be captured from measurements obtained at a sub-Nyquist rate. The decoding cost of compressive sensing, however, grows superlinearly with the problem size. In distributed sensor systems, the aggregate amount of compressive measurements encoded by the sensors can be substantial, and the decode cost for all the variables involved can be large. In this paper we propose a method to combine measurements from distributed sensors. With our method we can transport and store a single combined measurement set, rather than multiple sets for all sensors. We show that via source separation and joint decoding, it is possible to recover an approximate to the original signal from combined measurements using progressive reconstruction which focuses on individual sensors. This results in a reduction in the number of variables used in decoding and consequently a reduced decoding time. We show that the computed approximation to the signal can still have sufficient accuracy for target detection. We describe the combining approach and the associated progressive reconstruction, and we illustrate them with image recovery for simple target detection examples.
Starting Page	163
Ending Page	168
File Size	812222
Page Count	6
File Format	PDF
ISBN	9781467300797
ISSN	21557578
e-ISBN	9781467300810
DOI	10.1109/MILCOM.2011.6127545
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decoding Image reconstruction Encoding Vectors Dictionaries Compressed sensing Interference
Content Type	Text
Resource Type	Article

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