Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration

作     者：Bingbing GAO Wenmin LI Gaoge HU Yongmin ZHONG Xinhe ZHU Bingbing GAO;Wenmin LI;Gaoge HU;Yongmin ZHONG;Xinhe ZHU

作者机构：School of AutomationNorthwestern Polytechnical UniversityXi’an 710072China School of EngineeringRMIT UniversityBundooraVIC 3083Australia 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2022年第35卷第5期

页      面：114-128页

核心收录：

学科分类：1101[军事学-军事思想及军事历史] 08[工学] 081105[工学-导航、制导与控制] 0825[工学-航空宇航科学与技术] 0701[理学-数学] 0811[工学-控制科学与工程] 

基　　金：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) 

主　　题：Autonomous integration Fading factor Hypersonic vehicle Inertial navigation systems Kalman filters Mahalanobis distance 

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 ***,INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity,non-additive noise and dynamic *** 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 *** combines the state-augmentation technique into the nonlinear CKF to decrease the negative effect of non-additive noise in inertial ***,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 *** 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.