Ecole d'ingénieur et centre de recherche en Sciences du numérique

Steady-state performance comparison of bayesian and standard adaptive filtering

Sadiki, Tayeb;Slock, Dirk T M

Asilomar 2006, 40th IEEE Annual Asilomar Conference on Signals, Systems, and Computers, October 29-November 1, 2006, Pacific Grove, USA

Student paper contest finalist

It has been known for a long time that for best tracking results adaptive filtering should be formulated as a Kalman filtering problem, leading to Bayesian Adaptive Filtering (BAF). BAF techniques with acceptable complexity can be obtained by focusing on a diagonal AR(1) model for the time-varying optimal filter settings. The hyper-parameters of the AR(1) model can be adapted by introducing EM techniques and one sample fixed-lag smoothing at little extra cost. Standard AF techniques such as the LMS and RLS algorithms are equipped with only one hyper-parameter (stepsize, forgetting factor) to optimize their tracking behavior. In this paper we compare the steady-state tracking performance of Bayesian and standard AF techniques.

Document Doi Bibtex

Titre:Steady-state performance comparison of bayesian and standard adaptive filtering
Mots Clés:Bayesian Adaptive Filter (BAF);LMS RLS and Kalman algorithms;Tracking ability;Timevarying system;Steady state analysis
Type:Conférence
Langue:English
Ville:Pacific Grove
Pays:ÉTATS-UNIS
Date:
Département:Systèmes de Communication
Eurecom ref:2115
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Bibtex: @inproceedings{EURECOM+2115, doi = {http://dx.doi.org/10.1109/ACSSC.2006.356626 }, year = {2006}, title = {{S}teady-state performance comparison of bayesian and standard adaptive filtering}, author = {{S}adiki, {T}ayeb and {S}lock, {D}irk {T} {M}}, booktitle = {{A}silomar 2006, 40th {IEEE} {A}nnual {A}silomar {C}onference on {S}ignals, {S}ystems, and {C}omputers, {O}ctober 29-{N}ovember 1, 2006, {P}acific {G}rove, {USA}}, address = {{P}acific {G}rove, {\'{E}}{TATS}-{UNIS}}, month = {11}, url = {http://www.eurecom.fr/publication/2115} }
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