State Estimation with Missing Measurements Using IMM

被引：0

作者：

Zheng, Shiyou ^{[1
]}

Tang, Xiaofang ^{[2
]}

Liu, Meiqin ^{[2
]}

Zhang, Senlin ^{[2
]}

Sheng, Weihua ^{[3
]}

机构：

[1] Radar & Avion Inst A VIC, Aviat Key Lab Sci & Technol AISSS, Wuxi 214063, Peoples R China

[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China

[3] Oklahoma State Univ, Sch Elect & Comp Engn, Stillwater, OK 74078 USA

来源：

2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2013年

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

Interacting Multiple Model Estimation (IMM); Measurements Missing; Gaussian Mixture; Nonlinear System; VARIABLE-STRUCTURE; SYSTEMS; TRACKING; TARGET;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：1868 / 1873

页数：6

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