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.
Type:
Conférence
City:
Pacific Grove
Date:
2006-11-01
Department:
Systèmes de Communication
Eurecom Ref:
2115
Copyright:
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