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The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds †

Hybrid systems are subject to multiple dynamic models, or so-called modes. To estimate the state, the sequence of modes has to be estimated, which results in an exponential growth of possible sequences. The most prominent solution to handle this is the interacting multiple model filter, which can be...

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Detalles Bibliográficos
Autores principales: Koller, Tom L., Frese, Udo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235476/
https://www.ncbi.nlm.nih.gov/pubmed/34204505
http://dx.doi.org/10.3390/s21124164
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author Koller, Tom L.
Frese, Udo
author_facet Koller, Tom L.
Frese, Udo
author_sort Koller, Tom L.
collection PubMed
description Hybrid systems are subject to multiple dynamic models, or so-called modes. To estimate the state, the sequence of modes has to be estimated, which results in an exponential growth of possible sequences. The most prominent solution to handle this is the interacting multiple model filter, which can be extended to smoothing. In this paper, we derive a novel generalization of the interacting multiple filter and smoother to manifold state spaces, e.g., quaternions, based on the boxplus-method. As part thereof, we propose a linear approximation to the mixing of Gaussians and a Rauch–Tung–Striebel smoother for single models on boxplus-manifolds. The derivation of the smoother equations is based on a generalized definition of Gaussians on boxplus-manifolds. The three, novel algorithms are evaluated in a simulation and perform comparable to specialized solutions for quaternions. So far, the benefit of the more principled approach is the generality towards manifold state spaces. The evaluation and generic implementations are published open source.
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spelling pubmed-82354762021-06-27 The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds † Koller, Tom L. Frese, Udo Sensors (Basel) Article Hybrid systems are subject to multiple dynamic models, or so-called modes. To estimate the state, the sequence of modes has to be estimated, which results in an exponential growth of possible sequences. The most prominent solution to handle this is the interacting multiple model filter, which can be extended to smoothing. In this paper, we derive a novel generalization of the interacting multiple filter and smoother to manifold state spaces, e.g., quaternions, based on the boxplus-method. As part thereof, we propose a linear approximation to the mixing of Gaussians and a Rauch–Tung–Striebel smoother for single models on boxplus-manifolds. The derivation of the smoother equations is based on a generalized definition of Gaussians on boxplus-manifolds. The three, novel algorithms are evaluated in a simulation and perform comparable to specialized solutions for quaternions. So far, the benefit of the more principled approach is the generality towards manifold state spaces. The evaluation and generic implementations are published open source. MDPI 2021-06-17 /pmc/articles/PMC8235476/ /pubmed/34204505 http://dx.doi.org/10.3390/s21124164 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Koller, Tom L.
Frese, Udo
The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds †
title The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds †
title_full The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds †
title_fullStr The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds †
title_full_unstemmed The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds †
title_short The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds †
title_sort interacting multiple model filter and smoother on boxplus-manifolds †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235476/
https://www.ncbi.nlm.nih.gov/pubmed/34204505
http://dx.doi.org/10.3390/s21124164
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