Multilevel mixture Kalman filter
type: Item
instance of: scholarly article
title: Multilevel mixture Kalman filter
zbMATH Open document ID: 1175.93223
DOI: 10.1155/S1110865704403229
author: Dong Guo
author: Rong Chen
author: Xiaodong Wang
published in: EURASIP Journal on Applied Signal Processing
publication date: 2005-05-03T00:00:00Z
review text: The authors consider the model \[ x_t= F_{\lambda t} x_{t-1}+ G_{\lambda_t} u_t,\qquad y_t= H_{\lambda_t}x_t+ K_{\lambda_t} v_t, \] where \(u_t\) and \(v_t\) are the state and observation noise respectively, and \(\lambda_t\) is a sequence of random indicator variables which form a Markov chain. The matrices \(F_{\lambda_t}\), \(G_{\lambda_t}\), \(H_{\lambda_t}\), and \(K_{\lambda_t}\) are known given \(\lambda_t\). The authors apply a mixture Kalman filter which generates Monte Carlo samples only in the indicator space and uses a mixture of Gaussian distributions to approximate the target conditional distribution. The complexity of the filter is quite high if the dimension of the indicator sampling space is high. In the case of the hierarchical structure of the indicator space, the authors address this difficulty by constructing a new filter, namely, the multilevel mixture Kalman filter.
reviewed by: Grigori N. Milstein
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 2163857
zbMATH Keywords: Bayesian filtering
zbMATH Keywords: sequential Monte Carlo
zbMATH Keywords: mixture Kalman filter
zbMATH Keywords: Hiddent Markov chain
MaRDI profile type: Publication
full work available at URL: s1110865704403229
OpenAlex ID: W2119707516
description: scientific article; zbMATH DE number 2163857