Journal ArticleDOI
Interacting multiple model particle filter
Yvo Boers,J.N. Driessen +1 more
- Vol. 150, Iss: 5, pp 344-349
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TLDR
In this article, a new method for multiple model particle filtering for Markovian switching systems is presented, which is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter.Abstract:
A new method for multiple model particle filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running. The regularised particle filter probability density is a mixture of Gaussian probability densities. The proposed method is able to deal with nonlinearities and non-Gaussian noise. Furthermore, the new method keeps a fixed number of particles in each mode, and therefore it does not suffer from the potential drawbacks of existing multiple model particle filters for Markovian switching systems.read more
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Exact Bayesian and particle filtering of stochastic hybrid systems
Henk A. P. Blom,E.A. Bloem +1 more
TL;DR: In this article, an interacting multiple model (IMM) particle filter (IMMPF) was proposed for a discrete-time stochastic hybrid system, where each particle consists of two components, one assuming Euclidean values, and the other assuming discrete mode values.
References
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Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Journal ArticleDOI
The interacting multiple model algorithm for systems with Markovian switching coefficients
TL;DR: In this paper, a novel approach to hypotheses merging is presented for linear systems with Markovian switching coefficients (dynamic multiple model systems) which is necessary to limit the computational requirements.
Journal ArticleDOI
Interacting multiple model methods in target tracking: a survey
TL;DR: The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems, with special attention to the assumptions underlying each algorithm and its applicability to various situations.