In this paper, we investigate some of the stochastic properties of two recently introduced multistage adaptive filtering algorithms, namely the LMS-Bayesian and the RLS-Bayesian algorithms. We study probability-1 convergence of these algorithm and derive their final mean squared error for stationary Gaussian time series. We will show that under some general independence assumptions, both algorithms are convergent in a probability-1 sense and achieve the performance of the best algorithm used in the mixture.

Original languageEnglish (US)
Pages (from-to)II/1329-II/1332
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2002
Event2002 IEEE International Conference on Acoustic, Speech and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Further results in multistage adaptive filtering'. Together they form a unique fingerprint.

  • Cite this