A Novel Bayesian Approach for the Two-Dimensional Harmonic Retrieval Problem

Rohan R. Pote; Bhaskar D. Rao

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arXiv · 2102.08515

A Novel Bayesian Approach for the Two-Dimensional Harmonic Retrieval Problem

Rohan R. Pote·Bhaskar D. Rao

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Abstract

Sparse signal recovery algorithms like sparse Bayesian learning work well but the complexity quickly grows when tackling higher dimensional parametric dictionaries. In this work we propose a novel Bayesian strategy to address the two dimensional harmonic retrieval problem, through remodeling and reparameterization of the standard data model. This new model allows us to introduce a block sparsity structure in a manner that enables a natural pairing of the parameters in the two dimensions. The numerical simulations demonstrate that the inference algorithm developed (H-MSBL) does not suffer from source identifiability issues and is capable of estimating the harmonic components in challenging scenarios, while maintaining a low computational complexity.

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BibTeX RIS

Rohan R. Pote, Bhaskar D. Rao. 2021-02-17. A Novel Bayesian Approach for the Two-Dimensional Harmonic Retrieval Problem. https://arxiv.org/abs/2102.08515

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