Fast Variational Block-Sparse Bayesian Learning

This repository contains the matlab implementation of the fast variational block-sparse Bayesian learning algorithm (FV-BSBL) described in [1].

Repository Structure

- fastBSBL/: Folder containing the algorithm described in [1]

- fastBSBL/fastBSBL.m: main file of the algorithm

- BSBL_public/: Folder containing the comparison algorithms BSBL-EM and BSBL-BO described in [2], copied from http://dsp.ucsd.edu/~zhilin/BSBL.html

- SBL_MV_matlab/: Folder containing the DOA-SBL comparison algorithm decsribed in [3], copied from https://github.com/gerstoft/SBL

- testFastBSBL.m: Demo script that generates a signal based on a Gaussian dictionary and blocks-sparse weights and applies the algorithms [1] and [2].

- application_DoA.m: Demo script how to apply the algorithm to Direction-of-Arrival (DOA) estimation and compares the performance to [3]

- plotThresholding.m: Script to generate [1, Figure 1].

- ospa_dist.m: function to calculate the OSPA distance, needed for application_DoA.m

- munkres.m: auxilary function required for ospa_dist.m

References

[1] J. Möderl, F. Pernkopf, K. Witrisal and E. Leitinger, "Fast Variational Block-Sparse Bayesian Learning", arXiv:2306.00442, 2023.

[2] Z. Zhang and B. D. Rao, "Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation," IEEE Trans. Signal Process., vol. 61, no. 8, pp. 2009–2015, Apr. 2013.

[3] P. Gerstoft, C. F. Mecklenbräuker, A. Xenaki, and S. Nannuru, "Multisnapshot sparse Bayesian learning for DOA,” IEEE Signal Process. Lett., vol. 23, no. 10, pp. 1469–1473, Oct. 2016.