Skip to main content
  • Research Article
  • Published:

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

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Petar M. Djurić.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Djurić, P.M., Kotecha, J.H., Esteve, F. et al. Sequential Parameter Estimation of Time-Varying Non-Gaussian Autoregressive Processes. EURASIP J. Adv. Signal Process. 2002, 262156 (2002). https://doi.org/10.1155/S1110865702205089

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1155/S1110865702205089

Keywords