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  • Research Article
  • Open Access

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

  • 1Email author,
  • 2,
  • 3 and
  • 3
EURASIP Journal on Advances in Signal Processing20022002:262156

https://doi.org/10.1155/S1110865702205089

  • Received: 1 August 2001
  • Published:

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, Stony Brook, NY 11794, USA
(2)
Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI 53706, USA
(3)
ENSEEIHT/TéSA 2, rue Charles Camichel, BP 7122, Toulouse Cedex 7, 31071, France

Copyright

© Djurić et al. 2002

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