Sequential Variational Bayesian estimation for directions-of-arrival

A set of MATLAB codes for direction-of-arrival (DOA) estimation, beamforming.

Features

The codes provide:

-Beamforming using variational Bayesian line spectral estimation (VALSE) [Badiu 16]

-Beamforming using sequential VALSE (SVALSE)

Citation

-Y. Park, F. Meyer, and P. Gerstoft, “Graph-based sequential beamforming,” J. Acoust. Soc. Am. 153(1) (2023).
-F. Meyer, Y. Park, and P. Gerstoft, “Variational Bayesian estimation of time-varying DOAs,” in Proc. IEEE FUSION (2020), pp. 1–6.
-Y. Park, F. Meyer, and P. Gerstoft, “Learning-Aided Initialization for Variational Bayesian DOA Estimation,” in Proc. IEEE ICASSP (2022), pp. 4938–4942.

[pdf]

-M.-A. Badiu, T. L. Hansen, and B. H. Fleury, “Variational Bayesian inference of line spectra,” IEEE Trans. Signal Process. 65(9) (2017), pp. 2247–2261.
-J. Zhu, Q. Zhang, P. Gerstoft, M.-A. Badiu, and Z. Xu, “Grid-less variational Bayesian line spectral estimation with multiple measurement vectors,” Signal Processing 161 (2019), pp. 155–164.

Sparse Bayesian learning (SBL) implementation is also available. [CODE]
-Y. Park, F. Meyer, and P. Gerstoft, “Sequential sparse Bayesian learning for time-varying direction of arrival,” J. Acoust. Soc. Am. 149(3) (2021).

Updates

Version 1.0: (01/21/2023 by Y. Park)

Contact

Yongsung Park, Florian Meyer, & Peter Gerstoft
MPL/SIO/UCSD
[email protected]
[email protected]
[email protected]

Noiselab / F. Meyer's lab