Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration
The INS/GNSS integration is the commonly used technique for hypersonic vehicle navigation. However, owing to the complicated flight dynamics with high maneuverability and large flight envelope, the dynamic model of INS/GNSS integration inevitably exists errors which degrades the navigation performan...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8945140/ |
| _version_ | 1831645101323976704 |
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| author | Gaoge Hu Longqiang Ni Bingbing Gao Xinhe Zhu Wei Wang Yongmin Zhong |
| author_facet | Gaoge Hu Longqiang Ni Bingbing Gao Xinhe Zhu Wei Wang Yongmin Zhong |
| author_sort | Gaoge Hu |
| collection | DOAJ |
| description | The INS/GNSS integration is the commonly used technique for hypersonic vehicle navigation. However, owing to the complicated flight dynamics with high maneuverability and large flight envelope, the dynamic model of INS/GNSS integration inevitably exists errors which degrades the navigation performance of a hypersonic vehicle seriously. In this paper, a new model predictive based unscented Kalman filter (MP-UKF) is proposed to address this problem. The MP-UKF employs the concept of model predictive filter for the establishment of a dynamic model error estimator, and it subsequently compensate the model error estimation to UKF for nonlinear state estimation. Since the MP-UKF could predict the dynamic model error persistently and correct the filtering procedure of UKF online, it improves the UKF adaptiveness and is promising for the performance enhancement of INS/GNSS integration for hypersonic vehicle navigation. Simulation results and comparison analysis have been conducted to demonstrate the effectiveness of the proposed method. |
| first_indexed | 2024-12-19T13:29:45Z |
| format | Article |
| id | doaj.art-e2c403da43184ccab446cf1989d4ae7c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:29:45Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e2c403da43184ccab446cf1989d4ae7c2022-12-21T20:19:26ZengIEEEIEEE Access2169-35362020-01-0184814482310.1109/ACCESS.2019.29628328945140Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS IntegrationGaoge Hu0https://orcid.org/0000-0002-6173-8426Longqiang Ni1https://orcid.org/0000-0003-0331-6804Bingbing Gao2https://orcid.org/0000-0002-6562-9315Xinhe Zhu3https://orcid.org/0000-0002-0755-1759Wei Wang4https://orcid.org/0000-0002-4566-5335Yongmin Zhong5https://orcid.org/0000-0002-0105-9296School of Automation, Northwestern Polytechnical University, Xi’an, ChinaNorthwest Institute of Mechanical and Engineering, Xianyang, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Engineering, RMIT University, Bundoora, VIC, AustraliaCRRC Yongji Electric Company, Xi’an, ChinaSchool of Engineering, RMIT University, Bundoora, VIC, AustraliaThe INS/GNSS integration is the commonly used technique for hypersonic vehicle navigation. However, owing to the complicated flight dynamics with high maneuverability and large flight envelope, the dynamic model of INS/GNSS integration inevitably exists errors which degrades the navigation performance of a hypersonic vehicle seriously. In this paper, a new model predictive based unscented Kalman filter (MP-UKF) is proposed to address this problem. The MP-UKF employs the concept of model predictive filter for the establishment of a dynamic model error estimator, and it subsequently compensate the model error estimation to UKF for nonlinear state estimation. Since the MP-UKF could predict the dynamic model error persistently and correct the filtering procedure of UKF online, it improves the UKF adaptiveness and is promising for the performance enhancement of INS/GNSS integration for hypersonic vehicle navigation. Simulation results and comparison analysis have been conducted to demonstrate the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/8945140/Unscented Kalman filtermodel predictive filterhypersonic vehicle navigationINS/GNSS integrationdynamic model error |
| spellingShingle | Gaoge Hu Longqiang Ni Bingbing Gao Xinhe Zhu Wei Wang Yongmin Zhong Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration IEEE Access Unscented Kalman filter model predictive filter hypersonic vehicle navigation INS/GNSS integration dynamic model error |
| title | Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration |
| title_full | Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration |
| title_fullStr | Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration |
| title_full_unstemmed | Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration |
| title_short | Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration |
| title_sort | model predictive based unscented kalman filter for hypersonic vehicle navigation with ins gnss integration |
| topic | Unscented Kalman filter model predictive filter hypersonic vehicle navigation INS/GNSS integration dynamic model error |
| url | https://ieeexplore.ieee.org/document/8945140/ |
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