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Bayesian methods for sparse RLS adaptive filters

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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