A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System  

作　　者：Fangfang Zhao Cuiqiao Chen Wei He Shuzhi Sam Ge 

机构地区：[1]School of Computer Science and Engineering,and Center for Robotics,University of Electronic Science and Technology of China,Chengdu 611731,China [2]School of Automation and Electrical Engineering,University of Science and Technology of Beijing,Beijing 100083,China [3]Social Robotics Laboratory,Interactive Digital Media Institute,and Department of Electrical and Computer Engineering,National University of Singapore,Singapore 117576,Singapore [4]Singapore 117576,Singapore

出　　处：《IEEE/CAA Journal of Automatica Sinica》2018年第6期1113-1120,共8页自动化学报（英文版）

基　　金：supported by the National Basic Research Program of China(973Program)(2014CB744206)

摘　　要：This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.This paper explores multiple model adaptive estimation（MMAE） method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter- multiple model adaptive estimation unscented Kalman filter（MMAE-UKF） rather than conventional Kalman filter methods,like the extended Kalman filter（EKF） and the unscented Kalman filter（UKF）. UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system（SINS） and celestial navigation system（CNS） by a ballistic missile＇s motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

关 键 词：Celestial navigation integrated navigation multiple model adaptive estimation unscented Kalman filter （MMAEUKF） strap-down inertial navigation 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]