Monte Carlo filters for non-linear state estimation

Bølviken, Erik; Acklam, Peter J.; Christophersen, Nils; StøRdal, John-Mikal

doi:10.1016/s0005-1098(00)00151-5

Cited by 61 publications

(35 citation statements)

References 20 publications

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“…[12], [13], [14], [15]) is a simulation-based method for general nonlinear nonGaussian state estimation, which attempts to approximate the complete probability density function (pdf) of the state to be estimated. This is in contrast to just estimating the first few central moments, as done for the EKF.…”

Section: B Rao-blackwellized Particle Filtering (Rbpf)-based Methods mentioning

confidence: 99%

Parameter Estimation Techniques for Helicopter Dynamic Modelling

Postlethwaite

Turner

2007

2007 American Control Conference

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Abstract-This paper investigates the problem of estimating parameters in a state-space helicopter dynamical model for helicopter controller design and evaluation. Two methods, PEM (the prediction error method) and RBPF (the RaoBlackwellized particle filter-based method), are presented in the paper. Computer simulations are carried out to assess and compare the performance of the two methods with different assumptions on the initial conditions of the parameters to be estimated. Based on this assessment, an estimation scheme that combines the two methods is proposed. The proposed scheme is then applied to real data from EH101 helicopter flight tests and a vertical model including rotor coning dynamics is identified.

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Section: B Rao-blackwellized Particle Filtering (Rbpf)-based Methods mentioning

confidence: 99%

Parameter Estimation Techniques for Helicopter Dynamic Modelling

Postlethwaite

Turner

2007

2007 American Control Conference

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“…This is different from representing densities V. Klumpp and U. D. Hanebeck are with the Intelligent Sensor-ActuatorSystems Laboratory (ISAS), Institute of Computer Science and Engineering, Universität Karlsruhe (TH), Germany klumpp@ira.uka.de, uwe.hanebeck@ieee.org by means of random samples [4], which is used by the popular particle filters [5], where the appropriate density parameters, i.e., weights and locations of the particles, are typically calculated by means of Monte Carlo techniques [6], [7].…”

Section: A Motivationmentioning

confidence: 99%

Dirac mixture trees for fast suboptimal multi-dimensional density approximation

Klumpp

Hanebeck

2008

2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

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Abstract-We consider the problem of approximating an arbitrary multi-dimensional probability density function by means of a Dirac mixture density. Instead of an optimal solution based on minimizing a global distance measure between the true density and its approximation, a fast suboptimal anytime procedure is proposed, which is based on sequentially partitioning the state space and component placement by local optimization. The proposed procedure adaptively covers the entire state space with a gradually increasing resolution. It can be efficiently implemented by means of a pre-allocated tree structure in a straightforward manner. The resulting computational complexity is linear in the number of components and linear in the number of dimensions. This allows a large number of components to be handled, which is especially useful in high-dimensional state spaces.

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“…When the nonlinearity system is strong and non-Gaussian distributions, the performance of EKF will descend or even become divergent [9,10,13]. For that the fault detection for nonlinear stochastic systems is known as a difficult problem and very few results are available [8,15]. General solution of the state estimation problem is described by the Bayesian recursive relations.…”

Section: Introductionmentioning

confidence: 99%

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

Souibgui¹,

Hmida²,

Châari³

2012

AJSP

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This paper introduces the particle-filtering (PF) based framework for fault diagnosis in non-linear systems and noise and disturbances being Gaussian. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distributions of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. We compare the fault detection performance with that using the extended Kalman filtering and investigate the isolation performance on a nonlinear system.

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Monte Carlo filters for non-linear state estimation

Cited by 61 publications

References 20 publications

Parameter Estimation Techniques for Helicopter Dynamic Modelling

Parameter Estimation Techniques for Helicopter Dynamic Modelling

Dirac mixture trees for fast suboptimal multi-dimensional density approximation

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

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