A sliding-window online fast variational sparse Bayesian learning algorithm

In this work a new online learning algorithm that uses automatic relevance determination (ARD) is proposed for fast adaptive nonlinear filtering. A sequential decision rule for inclusion or deletion of basis functions is obtained by applying a recently proposed fast variational sparse Bayesian learning (SBL) method. The proposed scheme uses a sliding window estimator to process the data in an online fashion. The noise variance can be implicitly estimated by the algorithm. It is shown that the described method has better mean square error (MSE) performance than a state of the art kernel recursive least squares (Kernel-RLS) algorithm when using the same number of basis functions.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Thomas Buchgraber, Dmitriy Shutin, H. Vincent Poor
• Keywords: Algorithm, Basis Functions, Fast Variational Sparse, ICASSP 2011, Signal Processing