DOA M-Estimation Using Sparse Bayesian Learning

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A4 Artikkeli konferenssijulkaisussa
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Date

2022

Department

Department of Signal Processing and Acoustics

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Mcode

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Language

en

Pages

5

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2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings, pp. 4933-4937, IEEE International Conference on Acoustics, Speech and Signal Processing ; Volume 2022-May

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.

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Publisher Copyright: © 2022 IEEE

Keywords

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

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DOI

10.1109/ICASSP43922.2022.9746740

Citation

Mecklenbräuker, C F, Gerstoft, P & Ollila, E 2022, DOA M-Estimation Using Sparse Bayesian Learning . in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings . IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2022-May, IEEE, pp. 4933-4937, IEEE International Conference on Acoustics, Speech, and Signal Processing, Singapore, Singapore, 23/05/2022 . https://doi.org/10.1109/ICASSP43922.2022.9746740

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https://urn.fi/URN:NBN:fi:aalto-202208104645

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[article-cris] Sähkötekniikan korkeakoulu / ELEC