Open Access

Sequential Parameter Estimation of Time-Varying Non-Gaussian Autoregressive Processes

  • Petar M. Djurić1Email author,
  • Jayesh H. Kotecha2,
  • Fabien Esteve3 and
  • Etienne Perret3
EURASIP Journal on Advances in Signal Processing20022002:262156

https://doi.org/10.1155/S1110865702205089

Received: 1 August 2001

Published: 12 August 2002

Abstract

Parameter estimation of time-varying non-Gaussian autoregressive processes can be a highly nonlinear problem. The problem gets even more difficult if the functional form of the time variation of the process parameters is unknown. In this paper, we address parameter estimation of such processes by particle filtering, where posterior densities are approximated by sets of samples (particles) and particle weights. These sets are updated as new measurements become available using the principle of sequential importance sampling. From the samples and their weights we can compute a wide variety of estimates of the unknowns. In absence of exact modeling of the time variation of the process parameters, we exploit the concept of forgetting factors so that recent measurements affect current estimates more than older measurements. We investigate the performance of the proposed approach on autoregressive processes whose parameters change abruptly at unknown instants and with driving noises, which are Gaussian mixtures or Laplacian processes.

Keywords

particle filtering sequential importance sampling forgetting factors Gaussian mixtures

Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, State University of New York at Stony Brook
(2)
Department of Electrical and Computer Engineering, University of Wisconsin
(3)
ENSEEIHT/TéSA 2

Copyright

© Djurić et al. 2002