Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise

This paper proposes a recursive elimination method for optimal filtering problems of a class of discrete-time nonlinear systems with non-Gaussian noise. By this method, most of the computations to solve an optimal filtering problem can be carried out off-line by using symbolic computation based on t...

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Main Authors: Tomoyuki Iori, Toshiyuki Ohtsuka
Format: Article
Language:English
Published: Taylor & Francis Group 2020-11-01
Series:SICE Journal of Control, Measurement, and System Integration
Subjects:
Online Access:http://dx.doi.org/10.9746/jcmsi.13.282
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author Tomoyuki Iori
Toshiyuki Ohtsuka
author_facet Tomoyuki Iori
Toshiyuki Ohtsuka
author_sort Tomoyuki Iori
collection DOAJ
description This paper proposes a recursive elimination method for optimal filtering problems of a class of discrete-time nonlinear systems with non-Gaussian noise. By this method, most of the computations to solve an optimal filtering problem can be carried out off-line by using symbolic computation based on the results from algebraic geometry. This property is suitable for moving horizon estimation, where a certain optimal filtering problem must be solved for different measurement sequences in each sampling interval. A numerical example is provided to compare the proposed method with other state estimation methods including the particle filter, and the efficiency of the proposed method is shown.
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spelling doaj.art-4ed72e5a5d72416cbf14ad372c08dba52023-10-12T13:43:55ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702020-11-0113628229010.9746/jcmsi.13.28212103313Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian NoiseTomoyuki Iori0Toshiyuki Ohtsuka1Department of Systems Science, Graduate School of Informatics, Kyoto UniversityDepartment of Systems Science, Graduate School of Informatics, Kyoto UniversityThis paper proposes a recursive elimination method for optimal filtering problems of a class of discrete-time nonlinear systems with non-Gaussian noise. By this method, most of the computations to solve an optimal filtering problem can be carried out off-line by using symbolic computation based on the results from algebraic geometry. This property is suitable for moving horizon estimation, where a certain optimal filtering problem must be solved for different measurement sequences in each sampling interval. A numerical example is provided to compare the proposed method with other state estimation methods including the particle filter, and the efficiency of the proposed method is shown.http://dx.doi.org/10.9746/jcmsi.13.282nonlinear estimationnon-gaussian distributionmoving horizon estimationcommutative algebra
spellingShingle Tomoyuki Iori
Toshiyuki Ohtsuka
Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise
SICE Journal of Control, Measurement, and System Integration
nonlinear estimation
non-gaussian distribution
moving horizon estimation
commutative algebra
title Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise
title_full Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise
title_fullStr Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise
title_full_unstemmed Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise
title_short Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise
title_sort recursive elimination method in moving horizon estimation for a class of nonlinear systems and non gaussian noise
topic nonlinear estimation
non-gaussian distribution
moving horizon estimation
commutative algebra
url http://dx.doi.org/10.9746/jcmsi.13.282
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AT toshiyukiohtsuka recursiveeliminationmethodinmovinghorizonestimationforaclassofnonlinearsystemsandnongaussiannoise