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IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Entropy-based learning of sensing matrices

Content Provider	IET Digital Library
Author	Parthasarathy, Gayatri Abhilash, G.
Abstract	Compressive sensing deals with non-uniform sampling of a signal, at an average rate much less than the Nyquist rate, and recovery of the signal uniquely from these reduced set of measurements. Construction of an efficient measurement (sensing) matrix to enable unique recovery is a topic of interest since compressive sensing came to existence. The sensing operator should be efficient to capture the vital information content of the signal into a reduced set of discrete measurements. This study proposes a learning method to construct an efficient sensing matrix, having orthogonal rows, for compressed sensing of a class of signals. The learning scheme identifies the sensing matrix by maximising the entropy of measurement vectors. The bounds on the entropy of the measurement vector necessary for the unique recovery of a signal are proposed. A comparison of the performance of the designed sensing matrix and the sensing matrices constructed using other existing methods is also presented. The simulation results on the recovery of synthetic, speech, and image signals, compressively sensed using the sensing matrix identified, show an improvement in the accuracy of recovery. The reconstruction quality is better, using less number of measurements than those measured using sensing matrices identified by other methods.
Starting Page	650
Ending Page	660
Page Count	11
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 7, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5078
Journal	IET Signal Processing
Publisher Date	2019-05-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Average Rate Compressed Sensing Compressive Sensing Designed Sensing Matrix Digital Signal Processing Discrete Measurement Efficient Measurement Matrix Efficient Sensing Matrix Entropy Entropy-based Learning Image Signal Knowledge Engineering Technique Learning in AI Learning Method Learning Scheme Matrix Algebra Measurement Vector Nonuniform Sampling Nyquist Rate Reduced Set Sensing Matrix Sensing Operator Signal Processing And Detection Signal Reconstruction Signal Sampling Statistics Unique Recovery
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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