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Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter

An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is...

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Autores principales: Sun, Bo, Zhang, Zhenwei, Liu, Shicai, Yan, Xiaobing, Yang, Chengxu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319541/
https://www.ncbi.nlm.nih.gov/pubmed/35890765
http://dx.doi.org/10.3390/s22145081
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author Sun, Bo
Zhang, Zhenwei
Liu, Shicai
Yan, Xiaobing
Yang, Chengxu
author_facet Sun, Bo
Zhang, Zhenwei
Liu, Shicai
Yan, Xiaobing
Yang, Chengxu
author_sort Sun, Bo
collection PubMed
description An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.
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spelling pubmed-93195412022-07-27 Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter Sun, Bo Zhang, Zhenwei Liu, Shicai Yan, Xiaobing Yang, Chengxu Sensors (Basel) Article An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability. MDPI 2022-07-06 /pmc/articles/PMC9319541/ /pubmed/35890765 http://dx.doi.org/10.3390/s22145081 Text en © 2022 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
Sun, Bo
Zhang, Zhenwei
Liu, Shicai
Yan, Xiaobing
Yang, Chengxu
Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter
title Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter
title_full Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter
title_fullStr Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter
title_full_unstemmed Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter
title_short Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter
title_sort integrated navigation algorithm based on multiple fading factors kalman filter
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319541/
https://www.ncbi.nlm.nih.gov/pubmed/35890765
http://dx.doi.org/10.3390/s22145081
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