Browsing by Author "Gerstoft, Peter"
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- DOA M-Estimation Using Sparse Bayesian Learning
A4 Artikkeli konferenssijulkaisussa(2022) Mecklenbräuker, Christoph F.; Gerstoft, Peter; Ollila, EsaRecent 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. - Qualitatively Robust Bayesian Learning for DOA from Array Data using M-Estimation of the Scatter Matrix
A4 Artikkeli konferenssijulkaisussa(2021) Mecklenbräuker, Christoph F.; Gerstoft, Peter; Ollila, EsaThe qualitative robustness of direction of arrival estimation using Sparse Bayesian Learning (SBL) is assessed by evaluating the corresponding empirical influence function (EIF). The EIF indicates that SBL is sensitive to deviations from the underlying joint Gaussian assumption on signal and noise. To improve its qualitative robustness, we modify SBL by plugging-in the sample covariance matrix of the phase-only array data instead of the conventional sample covariance. A qualitatively more robust DOA estimate is derived as maximum likelihood estimate based on the complex multivariate t-distribution as the model-distribution for array data. Finally, we discuss and compare the qualitative robustness of the derived DOA estimators by evaluating the corresponding EIFs. - Robust and sparse M-estimation of DOA
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-07) Mecklenbräuker, Christoph F.; Gerstoft, Peter; Ollila, Esa; Park, YongsungA robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate t-distribution (MVT) with ν degrees of freedom, as well as Huber and Tyler loss functions. For Gauss loss, the method reduces to Sparse Bayesian Learning (SBL). The root mean square DOA error of the derived estimators is discussed for Gaussian, MVT, and ϵ-contaminated data. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian array data.