Distributed Consensus-Based Unscented Kalman Filtering with Missing Measurements

被引：0
|
作者：
Niu, Yichun [1 ]
Sheng, Li [1 ]
机构：
[1] China Univ Petr East China, Coll Informat & Control Engn, Qingdao 266580, Peoples R China
来源：
PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年
基金：
中国国家自然科学基金;
关键词：
Consensus Algorithm; Unscented Kalman Filters; Distributed Sensor Networks; Missing Measurements; INTERMITTENT OBSERVATIONS; STOCHASTIC STABILITY; NETWORKS; TRACKING; SYSTEMS; STATE;
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：
In this paper, the state estimation problem is investigated for a class of nonlinear stochastic systems. In order to deal with the effects of missing measurements, a consensus-based unscented Kalman filtering algorithm is presented on the basis of distributed sensor networks. Moreover, a sufficient condition is derived to ensure that the estimate error is bounded in mean square. Finally, simulation results show that the proposed filters can estimate the true state of the target plant under undesirable conditions.
引用
收藏
页码：8993 / 8998
页数：6
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