NDLI: Pattern coupled sparse Bayesian learning for recovery of time varying sparse signals

Content Provider	IEEE Xplore Digital Library
Author	Jun Fang Yanning Shen Hongbin Li
Copyright Year	2014
Description	Author affiliation: Nat. Key Lab. on Commun., Univ. of Electron. Sci. & Technol. of China, Chengdu, China (Jun Fang; Yanning Shen) \|\| Dept. of Electr. & Comput. Eng., Stevens Inst. of Technol., Hoboken, NJ, USA (Hongbin Li)
Abstract	In this paper, we consider the problem of recovering time-varying sparse signals whose sparsity patterns change slowly over time. We develop a new sparse Bayesian learning method for recovery of time-varying sparse signals. A pattern-coupled hierarchical Gaussian prior model is introduced to capture the correlation of the temporal support of time-varying sparse signals. Like the conventional sparse Bayesian learning framework, a set of hyperparameters are introduced to control the sparsity of the signal coefficients. The notable difference is that, for our model, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters associated with the coefficients of neighboring temporal observations. In doing this way, sparsity patterns of adjacent (in time) coefficients are coupled through their shared hyperparameters. Hence the prior has the potential to encourage temporally correlated sparsity patterns, while without imposing any pre-defined structures on the recovered signals. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.
Starting Page	705
Ending Page	709
File Size	235153
Page Count	5
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900755
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayes methods Signal processing algorithms Digital signal processing Correlation Compressed sensing Covariance matrices Vectors
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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