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An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems

In this paper, a fixed-point iterative filter developed from the classical extended Kalman filter (EKF) was proposed for general nonlinear systems. As a nonlinear filter developed from EKF, the state estimate was obtained by applying the Kalman filter to the linearized system by discarding the highe...

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Detalles Bibliográficos
Autores principales: Feng, Xiaoliang, Feng, Yuxin, Wen, Chenglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515326/
https://www.ncbi.nlm.nih.gov/pubmed/31010066
http://dx.doi.org/10.3390/s19081893
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author Feng, Xiaoliang
Feng, Yuxin
Wen, Chenglin
author_facet Feng, Xiaoliang
Feng, Yuxin
Wen, Chenglin
author_sort Feng, Xiaoliang
collection PubMed
description In this paper, a fixed-point iterative filter developed from the classical extended Kalman filter (EKF) was proposed for general nonlinear systems. As a nonlinear filter developed from EKF, the state estimate was obtained by applying the Kalman filter to the linearized system by discarding the higher-order Taylor series items of the original nonlinear system. In order to reduce the influence of the discarded higher-order Taylor series items and improve the filtering accuracy of the obtained state estimate of the steady-state EKF, a fixed-point function was solved though a nested iterative method, which resulted in a fixed-point iterative filter. The convergence of the fixed-point function is also discussed, which provided the existing conditions of the fixed-point iterative filter. Then, Steffensen’s iterative method is presented to accelerate the solution of the fixed-point function. The final simulation is provided to illustrate the feasibility and the effectiveness of the proposed nonlinear filtering method.
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spelling pubmed-65153262019-05-30 An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems Feng, Xiaoliang Feng, Yuxin Wen, Chenglin Sensors (Basel) Article In this paper, a fixed-point iterative filter developed from the classical extended Kalman filter (EKF) was proposed for general nonlinear systems. As a nonlinear filter developed from EKF, the state estimate was obtained by applying the Kalman filter to the linearized system by discarding the higher-order Taylor series items of the original nonlinear system. In order to reduce the influence of the discarded higher-order Taylor series items and improve the filtering accuracy of the obtained state estimate of the steady-state EKF, a fixed-point function was solved though a nested iterative method, which resulted in a fixed-point iterative filter. The convergence of the fixed-point function is also discussed, which provided the existing conditions of the fixed-point iterative filter. Then, Steffensen’s iterative method is presented to accelerate the solution of the fixed-point function. The final simulation is provided to illustrate the feasibility and the effectiveness of the proposed nonlinear filtering method. MDPI 2019-04-21 /pmc/articles/PMC6515326/ /pubmed/31010066 http://dx.doi.org/10.3390/s19081893 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Feng, Xiaoliang
Feng, Yuxin
Wen, Chenglin
An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems
title An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems
title_full An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems
title_fullStr An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems
title_full_unstemmed An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems
title_short An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems
title_sort ekf-based fixed-point iterative filter for nonlinear systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515326/
https://www.ncbi.nlm.nih.gov/pubmed/31010066
http://dx.doi.org/10.3390/s19081893
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