### [Back]

Talks and Poster Presentations (with Proceedings-Entry):

C. Mecklenbräuker, P. Gerstoft, E. Ollila:
"DOA M-Estimation Using Sparse Bayesian Learning";
Poster: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore; 05-22-2022 - 05-27-2022; in: "Proceedings 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)", IEEE, (2022), ISBN: 978-1-5090-6631-5; Paper ID 1243, 5 pages.

English abstract:
Recent investigations indicate that Sparse Bayesian Learning (SBL) is lacking in robustness.
We derive a robust and sparse Direction of Arrival (DOA) estimation framework based on the assumption that the array data has a centered (zero-mean) complex elliptically symmetric (ES) distribution with finite second-order moments. In the derivation, the loss function can be quite general. We consider three specific choices: the ML-loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate $t$-distribution (MVT) with $\nu$ degrees of freedom, and the loss for Huber´s M-estimator. For Gaussian loss, the method reduces to the classic SBL method. The root mean square DOA performance of the derived estimators is discussed for Gaussian, MVT, and $\epsilon$-contaminated noise.
The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian noise.

Keywords:
DOA estimation, robust statistics, outliers, sparsity, Bayesian learning

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.