Light-Weight Sequential SBL Algorithm: An Alternative to OMP

We present a Light-Weight Sequential Sparse Bayesian Learning (LWS-SBL) algorithm as an alternative to the orthogonal matching pursuit (OMP) algorithm for the general sparse signal recovery problem. The proposed approach formulates the recovery problem under the Type-II estimation framework and the...

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Bibliographic Details
Published inICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
Main Authors Pote, Rohan R., Rao, Bhaskar D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.06.2023
Subjects
Bayes methods
compressed sensing
computational complexity
Dictionaries
gridless estimation
Matching pursuit algorithms
Maximum likelihood estimation
orthogonal matching pursuit
Position measurement
Signal processing
Signal processing algorithms
sparse Bayesian learning
Sparse signal recovery
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Summary:We present a Light-Weight Sequential Sparse Bayesian Learning (LWS-SBL) algorithm as an alternative to the orthogonal matching pursuit (OMP) algorithm for the general sparse signal recovery problem. The proposed approach formulates the recovery problem under the Type-II estimation framework and the stochastic maximum likelihood objective. We compare the computational complexity for the proposed algorithm with OMP and highlight the main differences. For the case of parametric dictionaries, a gridless version is developed by extending the proposed sequential SBL algorithm to locally optimize grid points near potential source locations and it is empirically shown that the performance approaches Cramer-Rao bound.´ Numerical results using the proposed approach demonstrate the support recovery performance improvements in different scenarios at a small computational price when compared to the OMP algorithm.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10096051