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C. Mecklenbräuker, P. Gerstoft, E. Ollila:
"DOA M-Estimation using Sparse Bayesian Learning";
Talk: Marine Physics Lab Seminar, University of California San Diego, University of California San Diego, La Jolla (CA), USA (invited); 03-01-2022.

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 ν 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 ε- contaminated noise. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian noise.

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