State Estimation with Missing Measurements Using IMM
作者单位：Aviation Key Laboratory of Science and Technology on AISSS Radar and avionics institute of AVIC College of Electrical Engineering Zhejiang University School of Electrical and Computer Engineering Oklahoma State University
会议名称：《第25届中国控制与决策会议》
会议届次：25th
主办单位：IEEE;NE Univ;IEEE Ind Elect Chapter;IEEE Harbin Sect Control Syst Soc Chapter;Guizhou Univ;IEEE Control Syst Soc;Syst Engn Soc China;Chinese Assoc Artificial Intelligence;Chinese Assoc Automat;Tech Comm Control Theory;Chinese Assoc Aeronaut;Automat Control Soc;Chinese Assoc Syst Simulat;Simulat Methods & Modeling Soc;Intelligent Control & Management Soc
会议日期：2013年
学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
基 金：supported in part by the National Natural Science Foundation of China under Grants 61222310, 61174142, 61071061, 61134012, and 60874050 the Zhejiang Provincial Natural Science Foundation of China under Grants R1100234 and Z1090423 the Program for New Century Excellent Talents (NCET) in University under Grant NCET-10-0692 the Fundamental Research Funds for the Central Universities under Grant 2011QNA4036 the ASFC under Grant 20102076002 the Specialized Research Fund for the Doctoral Program of Higher Education of China (SRFDP) under Grants 20100101110055 and 20120101110115 the Zhejiang Provincial Science and Technology Planning Projects of China under Grant 2012C21044 the Marine Interdisciplinary Research Guiding Funds for Zhejiang University under Grant 2012HY009B supported by the "151 Talent Project" of Zhejiang Province
关 键 词：Interacting Multiple Model Estimation (IMM) Measurements Missing Gaussian Mixture Nonlinear System
摘 要：The missing of the measurements will deteriorate the estimation or even make the estimators divergent. We first analyze the performance of the existed estimation approaches in nonlinear systems with missing measurements. According to the effectiveness of the multiple model estimation over single model estimation, we propose to give the estimates of the states using the interacting multiple model estimation （IMM）. The IMM contains two model sets. One of them corresponds to the systems modes and the other corresponds to the occurrence of the missing of the measurements. The simulation results show the proposed approach is more stable and accurate than the existed estimation approaches.