Dynamic Compressive Sensing of Time-Varying Signals Via Approximate Message Passing

In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective. While there has been a handful of previously proposed Bayesian dynamic CS algorithms in the li...

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Vydané v:	IEEE transactions on signal processing Ročník 61; číslo 21; s. 5270 - 5284
Hlavní autori:	Ziniel, Justin, Schniter, Philip
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.11.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Approximate message passing (AMP) Approximation Bayes methods Bayesian analysis belief propagation compressed sensing Correlation Detection Detection, estimation, filtering, equalization, prediction dynamic compressive sensing Dynamics Exact sciences and technology expectation-maximization algorithms Heuristic algorithms Inference algorithms Information, signal and communications theory Kalman ﬁlters Learning Markov processes Mathematical models Message passing Sampling, quantization Signal and communications theory Signal, noise statistical signal processing Telecommunications and information theory Time series analysis time-varying sparse signals Vectors Performance evaluation Parameter estimation State of the art belief propagation expectation-maximization algorithms Kalman filter Wireless telecommunication Signal estimation Frequency estimation Adaptive method Time variation Approximate message passing (AMP) Learning Credal approach Digital communication Dynamic model Oracle Kalman filters Mobile radiocommunication statistical signal processing Filtering Probabilistic approach time-varying sparse signals Computational complexity Message passing dynamic compressive sensing Distributed control Causality EM algorithm Compressed sensing
ISSN:	1053-587X, 1941-0476
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