Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals
Abstract
Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an algorithm for dynamic filtering of time-varying sparse signals based on the sparse Bayesian learning (SBL) framework. The key idea underlying the algorithm, termed SBL-DF, is the incorporation of a signal prediction generated from a dynamics model and estimates of...
Paper Details
Title
Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals
Published Date
Jan 1, 2020
Volume
68
Pages
388 - 403
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