Interacting Multiple Gaussian Particle Filter

DOI

10.2991/icaise.2013.15How to use a DOI?

Keywords

Interacting multiple model, Particle Fiter, Gaussian Particle Filter

Abstract

Inspired by the framework of the Interacting Multiple Model (IMM), a method, called Interacting Multiple Gaussian Particle Filter (IMGPF), is proposed for solving the nonlinear Bayesian filtering problem with unknown continuous parameter. IMGPF regards the continuous parameter space as a union of disjoint subspaces, and each subspace is assigned to a model respectively. At each time step, for each model of IMGPF, under the assumption that the parameter belongs to the corresponding subspace, a Gaussian Particle Filter is applied to estimate the parameter and the state together. The parameter of each model of IMM is a fixed value, while the parameter of each model of IMGPF is a random variable need to be estimated. Thus IMGPF can achieve better estimation performance than IMM when the true parameter does not close to any element of the IMM model set. A simulation example of bearings only tracking problem is presented to demonstrate the effectiveness of IMGPF.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanwen Qu
PY  - 2013/08
DA  - 2013/08
TI  - Interacting Multiple Gaussian Particle Filter
BT  - Proceedings of the 2013 The International Conference on Artificial Intelligence and Software Engineering (ICAISE 2013)
PB  - Atlantis Press
SP  - 62
EP  - 66
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaise.2013.15
DO  - 10.2991/icaise.2013.15
ID  - Qu2013/08
ER  -