| Paper ID | SAM-7.3 |
| Paper Title |
A NOVEL BAYESIAN APPROACH FOR THE TWO-DIMENSIONAL HARMONIC RETRIEVAL PROBLEM |
| Authors |
Rohan R. Pote, Bhaskar D. Rao, University of California, San Diego, United States |
| Session | SAM-7: Detection and Estimation 1 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Sensor Array and Multichannel Signal Processing: [SAM-CSSM] Compressed sensing and sparse modeling |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
