Learning-Aided Initialization for Variational Bayesian DOA Estimation

Yongsung Park; Florian Meyer; Peter Gerstoft

ICASSP 2022accepted0 citations

Learning-Aided Initialization for Variational Bayesian DOA Estimation

Yongsung Park, Florian Meyer, Peter Gerstoft

Abstract

We present a sparsity-promoting method for the detection and estimation of the directions of arrival (DOAs) of source signals. The proposed method is based on the recently introduced variational Bayesian line spectral estimation (VALSE) approach, which is gridless. However, the performance of VALSE is sensitive to an initial guess of the measurement noise variance and potential DOAs. Thus, we propose a sparse Bayesian learning-aided initialization. Simulation results show that this learning-aided VALSE outperforms state-of-the-art DOA estimation methods as well as the conventional VALSE. We also evaluate the proposed method using acoustic data from an ocean acoustics experiment.

BibTeX

@inproceedings{icassp2022_learningaidedini,
  title = {Learning-Aided Initialization for Variational Bayesian DOA Estimation},
  author = {Yongsung Park and Florian Meyer and Peter Gerstoft},
  booktitle = {ICASSP 2022},
  year = {2022}
}