<div class="csl-bib-body">
<div class="csl-entry">Mecklenbräuker, C., Gerstoft, P., Ollila, E., & Park, Y. (2024, February 18). <i>Convergence Analysis of Robust and Sparse M-Estimation of DOA</i> [Conference Presentation]. Information Theory and Applications Workshop 2024, San Diego, United States of America (the). https://doi.org/10.34726/8343</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/209464
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dc.identifier.uri
https://doi.org/10.34726/8343
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dc.description.abstract
Previously, a robust and sparse Direction of Arrival (DOA) estimator was derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments which is evaluated by iterations. Here, the convergence of the iterations is investigated analytically and numerically. The analytical investigation proceeds in two steps: Firstly it is shown that the true solution is a fixed point of the iteration. Then, it is shown that the iteration update decreases the DOA estimation error. Finally, the required number of iterations until convergence is evaluated numerically for exemplary DOA estimation scenarios and discussed
en
dc.language.iso
en
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dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
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dc.subject
SBL
en
dc.title
Convergence Analysis of Robust and Sparse M-Estimation of DOA
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.rights.license
Creative Commons Namensnennung - Nicht kommerziell 4.0 International
de
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
