Monte Carlo filters for non-linear state estimation

Authors:
Erik BøLviken;Peter J. Acklam;Nils Christophersen;John-Mikal StøRdal
Affiliations:
Department of Mathematics, University of Oslo, P.O. Box 1053, N-0316 Oslo, Norway;Department of Mathematics, University of Oslo, P.O. Box 1053, N-0316 Oslo, Norway;Department of Informatics, University of Oslo, Norway;The Norwegian Defence Research Establishment, Norway
Venue:
Automatica (Journal of IFAC)
Year:
2001

Citing 3
Cited 5

Recursive Bayesian estimation using piece-wise constant approximations

Automatica (Journal of IFAC)
Nonlinear and nonnormal filters using Monte Carlo methods

Computational Statistics & Data Analysis
Digital synthesis of non-linear filters

Automatica (Journal of IFAC)

Towards applications of particle filters in wildfire spread simulation

Proceedings of the 40th Conference on Winter Simulation
State estimation using particle filters in wildfire spread simulation

SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Set-membership fuzzy filtering for nonlinear discrete-time systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A dynamic data driven application system for wildfire spread simulation

Winter Simulation Conference
Brief Sonar-based robot navigation using nonlinear robust observers

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

It is shown through a simple mathematical formula that Monte Carlo computations of Bayesian filter estimates do not demand many repetitions. A general algorithm is constructed, and its performance on difficult problems demonstrated.