MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter

In order to improve the accuracy of MIMU/BDS integrated navigation with uncertain models and large disturbances, a smoothing variable structure-Kalman combined filter information fusion method is proposed. The coordinate transformation method is introduced, and the state space equation and the obser...

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Autor principal:	Li Can, Shen Qiang, Qin Weiwei, Duan Zhiqiang, Wang Lixin
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Aero Weaponry 2021
Materias:	\|mimu/bds integrated navigation\|uncertain model\|adaptive fading factor\|smooth variable structure filter\|combined filter algorithm Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/6b86e509c5d04e9890bb5b577f2f1881
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id	oai:doaj.org-article:6b86e509c5d04e9890bb5b577f2f1881
record_format	dspace
spelling	oai:doaj.org-article:6b86e509c5d04e9890bb5b577f2f18812021-11-30T00:13:41ZMIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter1673-504810.12132/ISSN.1673-5048.2020.0218https://doaj.org/article/6b86e509c5d04e9890bb5b577f2f18812021-06-01T00:00:00Zhttps://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/1628469427333-818649196.pdfhttps://doaj.org/toc/1673-5048In order to improve the accuracy of MIMU/BDS integrated navigation with uncertain models and large disturbances, a smoothing variable structure-Kalman combined filter information fusion method is proposed. The coordinate transformation method is introduced, and the state space equation and the observation equation of the integrated navigation system are established. In order to prevent the algorithm divergence caused by the old observation data, the adaptive fading factor which varies with the residual error is incorporated into the Kalman filter algorithm, and the adaptive Kalman filter algorithm is constructed. Combining the accuracy advantage of Kalman filter with the robustness advantage of smooth variable structure filter, a smooth variable structure-Kalman combined filter algorithm is constructed. It is verified by simulation that location and speed fusion error of combined algorithm are smaller than Kalman filter and adaptive Kalman filter under the condition of uncertain model and large disturbances. The experiment shows that location fusion accuracy and speed fusion accuracy are also high under the condition of satellite occlusion, which means the accurate navigation with insufficient number of satellites is achieved by utilizing the combined filter algorithm.Li Can, Shen Qiang, Qin Weiwei, Duan Zhiqiang, Wang LixinEditorial Office of Aero Weaponryarticle\|mimu/bds integrated navigation\|uncertain model\|adaptive fading factor\|smooth variable structure filter\|combined filter algorithmMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 3, Pp 51-58 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|mimu/bds integrated navigation\|uncertain model\|adaptive fading factor\|smooth variable structure filter\|combined filter algorithm Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|mimu/bds integrated navigation\|uncertain model\|adaptive fading factor\|smooth variable structure filter\|combined filter algorithm Motor vehicles. Aeronautics. Astronautics TL1-4050 Li Can, Shen Qiang, Qin Weiwei, Duan Zhiqiang, Wang Lixin MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter
description	In order to improve the accuracy of MIMU/BDS integrated navigation with uncertain models and large disturbances, a smoothing variable structure-Kalman combined filter information fusion method is proposed. The coordinate transformation method is introduced, and the state space equation and the observation equation of the integrated navigation system are established. In order to prevent the algorithm divergence caused by the old observation data, the adaptive fading factor which varies with the residual error is incorporated into the Kalman filter algorithm, and the adaptive Kalman filter algorithm is constructed. Combining the accuracy advantage of Kalman filter with the robustness advantage of smooth variable structure filter, a smooth variable structure-Kalman combined filter algorithm is constructed. It is verified by simulation that location and speed fusion error of combined algorithm are smaller than Kalman filter and adaptive Kalman filter under the condition of uncertain model and large disturbances. The experiment shows that location fusion accuracy and speed fusion accuracy are also high under the condition of satellite occlusion, which means the accurate navigation with insufficient number of satellites is achieved by utilizing the combined filter algorithm.
format	article
author	Li Can, Shen Qiang, Qin Weiwei, Duan Zhiqiang, Wang Lixin
author_facet	Li Can, Shen Qiang, Qin Weiwei, Duan Zhiqiang, Wang Lixin
author_sort	Li Can, Shen Qiang, Qin Weiwei, Duan Zhiqiang, Wang Lixin
title	MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter
title_short	MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter
title_full	MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter
title_fullStr	MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter
title_full_unstemmed	MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter
title_sort	mimu/bds integrated navigation technology based on smooth variable structure-adaptive kalman filter
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/6b86e509c5d04e9890bb5b577f2f1881
work_keys_str_mv	AT licanshenqiangqinweiweiduanzhiqiangwanglixin mimubdsintegratednavigationtechnologybasedonsmoothvariablestructureadaptivekalmanfilter
_version_	1718406868688175104

MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter

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