Koeppl, H. ; Kubin, G. ; Paoli, G. (2003)
Bayesian methods for sparse RLS adaptive filters.
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
This work deals with an extension of the standard recursive least squares (RLS) algorithm. It allows to prune irrelevant coefficients of a linear adaptive filter with sparse impulse response and it provides a regularization method with automatic adjustment of the regularization parameter. New update equations for the inverse auto-correlation matrix estimate are derived that account for the continuing shrinkage of the matrix size. In case of densely populated impulse responses of length M, the computational complexity of the algorithm stays O(M2) as for standard RLS while for sparse impulse responses the new algorithm becomes much more efficient through the adaptive shrinkage of the dimension of the coefficient space. The algorithm has been successfully applied to the identification of sparse channel models (as in mobile radio or echo cancellation).
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2003 |
| Autor(en): | Koeppl, H. ; Kubin, G. ; Paoli, G. |
| Art des Eintrags: | Bibliographie |
| Titel: | Bayesian methods for sparse RLS adaptive filters |
| Sprache: | Englisch |
| Publikationsjahr: | 2003 |
| Ort: | Pacific Grove, CA, USA |
| Verlag: | IEEE |
| Band einer Reihe: | 2 |
| Veranstaltungstitel: | The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 |
| URL / URN: | http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe... |
| Kurzbeschreibung (Abstract): | This work deals with an extension of the standard recursive least squares (RLS) algorithm. It allows to prune irrelevant coefficients of a linear adaptive filter with sparse impulse response and it provides a regularization method with automatic adjustment of the regularization parameter. New update equations for the inverse auto-correlation matrix estimate are derived that account for the continuing shrinkage of the matrix size. In case of densely populated impulse responses of length M, the computational complexity of the algorithm stays O(M2) as for standard RLS while for sparse impulse responses the new algorithm becomes much more efficient through the adaptive shrinkage of the dimension of the coefficient space. The algorithm has been successfully applied to the identification of sparse channel models (as in mobile radio or echo cancellation). |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik |
| Hinterlegungsdatum: | 04 Apr 2014 12:35 |
| Letzte Änderung: | 23 Sep 2021 14:32 |
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