NDLI: Compressive sensing least square problem solution suitable for implementation

Content Provider	IEEE Xplore Digital Library
Author	Draganic, A. Orovic, I. Stankovic, S.
Copyright Year	2015
Description	Author affiliation: Fac. of Electr. Eng., Univ. of Montenegro, Podgorica, Montenegro (Draganic, A.; Orovic, I.; Stankovic, S.)
Abstract	Compressive Sensing approach allows reconstruction of under-sampled sparse signals, by using different optimization techniques. These techniques solve undetermined systems of equations which may be recast as least square problems. Since there is a growing need for real-time hardware implementations of the reconstruction methods, it is important for these methods to be fast enough and not be computationally demanding. Here, we will focus on QR decomposition based approach for solving least square problems. Least square problem solution is defined in such way that does not require Q matrix, obtained as a result of QR decomposition of the measurement matrix, to be used in calculation and leads to the lower computational complexity.
Starting Page	376
Ending Page	379
File Size	159841
Page Count	4
File Format	PDF
e-ISBN	9781509000555
DOI	10.1109/TELFOR.2015.7377487
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-24
Publisher Place	Serbia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	QR decomposition Least square problem Transforms Compressive Sensing Matrix decomposition Givens rotations Computational complexity Optimization Classical Gram-Schmidt Modified Gram-Schmidt Hardware Mathematical model Compressed sensing
Content Type	Text
Resource Type	Article

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