<div class="csl-bib-body">
<div class="csl-entry">Mecklenbräuker, C., Gerstoft, P., Ollila, E., & Park, Y. (2023, June 16). <i>Robust and Sparse M-Estimation of DOA</i> [Poster Presentation]. International Seminar on Smart Wireless Communications 2023, Sophia-Antipolis, France. http://hdl.handle.net/20.500.12708/186981</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/186981
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dc.description.abstract
A 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 noise.
en
dc.language.iso
en
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dc.subject
M-estimation
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dc.title
Robust and Sparse M-Estimation of DOA
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dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
University of California, San Diego, United States of America (the)
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dc.contributor.affiliation
Aalto University, Finland
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dc.contributor.affiliation
University of California, San Diego, United States of America (the)
