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Research on IMU Calibration Model Based on Polar Decomposition

As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new...

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Autores principales: Zhao, Guiling, Tan, Maolin, Wang, Xu, Liang, Weidong, Gao, Shuai, Chen, Zhijian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056368/
https://www.ncbi.nlm.nih.gov/pubmed/36985104
http://dx.doi.org/10.3390/mi14030697
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author Zhao, Guiling
Tan, Maolin
Wang, Xu
Liang, Weidong
Gao, Shuai
Chen, Zhijian
author_facet Zhao, Guiling
Tan, Maolin
Wang, Xu
Liang, Weidong
Gao, Shuai
Chen, Zhijian
author_sort Zhao, Guiling
collection PubMed
description As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new IMU calibration model based on polar decomposition. Using the new model, the installation error is decomposed into a nonorthogonal error and a misalignment error. The compensation of the IMU calibration model is decomposed into two steps. First, the nonorthogonal error is compensated, and then the misalignment error is compensated. Based on the proposed IMU calibration model, we used a three-axis turntable to calibrate three sets of strapdown inertial navigation systems (SINS). The experimental results show that the misalignment errors are larger than the nonorthogonal errors. Based on the experimental results, this paper proposes a new method to simplify the installation error. This simplified method defines the installation error matrix as an antisymmetric matrix composed of three misalignment errors. The navigation errors caused by the proposed simplified calibration model are compared with the navigation errors caused by the traditional simplified calibration model. The 48-h navigation experiment results show that the proposed simplified calibration model is superior to the traditional simplified calibration model in attitude accuracy, velocity accuracy, and position accuracy.
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spelling pubmed-100563682023-03-30 Research on IMU Calibration Model Based on Polar Decomposition Zhao, Guiling Tan, Maolin Wang, Xu Liang, Weidong Gao, Shuai Chen, Zhijian Micromachines (Basel) Article As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new IMU calibration model based on polar decomposition. Using the new model, the installation error is decomposed into a nonorthogonal error and a misalignment error. The compensation of the IMU calibration model is decomposed into two steps. First, the nonorthogonal error is compensated, and then the misalignment error is compensated. Based on the proposed IMU calibration model, we used a three-axis turntable to calibrate three sets of strapdown inertial navigation systems (SINS). The experimental results show that the misalignment errors are larger than the nonorthogonal errors. Based on the experimental results, this paper proposes a new method to simplify the installation error. This simplified method defines the installation error matrix as an antisymmetric matrix composed of three misalignment errors. The navigation errors caused by the proposed simplified calibration model are compared with the navigation errors caused by the traditional simplified calibration model. The 48-h navigation experiment results show that the proposed simplified calibration model is superior to the traditional simplified calibration model in attitude accuracy, velocity accuracy, and position accuracy. MDPI 2023-03-21 /pmc/articles/PMC10056368/ /pubmed/36985104 http://dx.doi.org/10.3390/mi14030697 Text en © 2023 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
Zhao, Guiling
Tan, Maolin
Wang, Xu
Liang, Weidong
Gao, Shuai
Chen, Zhijian
Research on IMU Calibration Model Based on Polar Decomposition
title Research on IMU Calibration Model Based on Polar Decomposition
title_full Research on IMU Calibration Model Based on Polar Decomposition
title_fullStr Research on IMU Calibration Model Based on Polar Decomposition
title_full_unstemmed Research on IMU Calibration Model Based on Polar Decomposition
title_short Research on IMU Calibration Model Based on Polar Decomposition
title_sort research on imu calibration model based on polar decomposition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056368/
https://www.ncbi.nlm.nih.gov/pubmed/36985104
http://dx.doi.org/10.3390/mi14030697
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