| Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration |
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| 作者姓名： | Bingbing GAO Wenmin LI Gaoge HU Yongmin ZHONG Xinhe ZHU |
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| 作者单位： | 1. School of Automation, Northwestern Polytechnical University;2. School of Engineering, RMIT University |
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| 基金项目： | co-supported by the National Natural Science Foundation of China (Nos. 41904028, 42004021)；;the Natural Science Basic Research Plan in Shaanxi Province of China (Nos. 2020JQ-150, 2020JQ-234)；;the Soft Science Project of Xi’an Science and Technology Plan (No. XA2020RKXYJ-0150)； |
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| 摘 要： | Inertial Navigation System/Celestial Navigation System(INS/CNS) integration, especially for the tightly-coupled mode, provides a promising autonomous tactics for Hypersonic Vehicle(HV) in military demands. However, INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity, non-additive noise and dynamic complexity.This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter(C...
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| 收稿时间： | 28 February 2021 |
Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration |
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| Bingbing GAO,Wenmin LI,Gaoge HU,Yongmin ZHONG,Xinhe ZHU.Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration[J].Chinese Journal of Aeronautics,2022,35(5):114-128. |
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| Institution: | 1. School of Automation, Northwestern Polytechnical University, Xi’an 710072, China;2. School of Engineering, RMIT University, Bundoora, VIC 3083, Australia |
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| Abstract: | Inertial Navigation System/Celestial Navigation System (INS/CNS) integration, especially for the tightly-coupled mode, provides a promising autonomous tactics for Hypersonic Vehicle (HV) in military demands. However, INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity, non-additive noise and dynamic complexity. This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter (CKF) to handle the strong INS error model nonlinearity caused by HV’s high dynamics. It combines the state-augmentation technique into the nonlinear CKF to decrease the negative effect of non-additive noise in inertial measurements. Subsequently, a technique for the detection of dynamic model uncertainty is developed, and the augmented CKF is modified with fading memory to tackle dynamic model uncertainty by rigorously deriving the fading factor via the theory of Mahalanobis distance without artificial empiricism. Simulation results and comparison analysis prove that the proposed method can effectively curb the adverse impacts of non-additive noise and dynamic model uncertainty for inertial measurements, leading to improved performance for HV navigation with tightly-coupled INS/CNS integration. |
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| Keywords: | Autonomous integration Fading factor Hypersonic vehicle Inertial navigation systems Kalman filters Mahalanobis distance |
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