@inproceedings{33a6165550584c5fa5b2381107db375c,
title = "A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation",
abstract = "This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous parameter space to a fixed grid of points, thus restricting the solution space. In this work, we avoid discretization by working directly with the signal model containing parameterized atoms. Inspired by the {"}fast inference scheme{"} by Tipping and Faul we develop a novel sparse Bayesian learning (SBL) algorithm, which estimates the atom parameters along with the model order and weighting coefficients. Numerical experiments for spectral estimation with closely-spaced frequency components, show that the proposed SBL algorithm outperforms subspace and compressed sensing methods.",
keywords = "sparse decomposition, sparse bayesian learning, compressed sensing",
author = "Hansen, {Thomas Lundgaard} and Badiu, {Mihai Alin} and Fleury, {Bernard Henri} and Rao, {Bhaskar D.}",
year = "2014",
month = jun,
doi = "10.1109/SAM.2014.6882422",
language = "English",
isbn = "978-1-4799-1481-4",
series = "I E E E Workshop on Sensor Array and Multichannel Signal Processing",
publisher = "IEEE",
pages = "385--388",
booktitle = "Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014 IEEE 8th",
address = "United States",
note = "2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM ; Conference date: 22-06-2013 Through 25-06-2014",
}