Variable structure multiple model fixed-interval smoothing

作     者：Bolun ZHANG Yongxin GAO Zhansheng DUAN Bolun ZHANG;Yongxin GAO;Zhansheng DUAN

作者机构：Faculty of Electronic and Information EngineeringSchool of Automation Science and EngineeringXi’an Jiaotong UniversityXi'an 710049China 

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

年 卷 期：2023年第36卷第2期

页      面：139-148页

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：supported in part by the National Natural Science Foundation of China(No.61773306) the National Key Research and Development Plan,China(Nos.2021YFC2202600 and 2021YFC2202603) 

主　　题：Fixed-interval smoothing Model-set adaptation Multiple model estimation Smoothing algorithm Variable structure 

摘      要：This paper focuses on fixed-interval smoothing for stochastic hybrid *** the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are *** develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this *** propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation *** improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)*** turns out that the smoothing performance is further improved by the LMS due to less competition among *** results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.