Distributed diffusion unscented Kalman filtering based on covariance intersection with intermittent measurements

被引：32

作者：

Chen, Hao ^{[1
,2
,3
]}

Wang, Jianan ^{[1
]}

Wang, Chunyan ^{[1
]}

Shan, Jiayuan ^{[1
]}

Xin, Ming ^{[4
]}

机构：

[1] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China

[2] Beijing Inst Elect Syst Engn, Beijing 100854, Peoples R China

[3] State Key Lab Intelligent Mfg Syst Technol, Beijing 100854, Peoples R China

[4] Univ Missouri, Dept Mech & Aerosp Engn, Columbia, MO 65211 USA

来源：

AUTOMATICA | 2021年 / 132卷

基金：

中国国家自然科学基金;

关键词：

Diffusion filtering; UKF; Covariance intersection; Intermittent measurements; ADAPTATION STRATEGIES; STOCHASTIC STABILITY; SENSOR NETWORKS; CONSENSUS;

D O I：

10.1016/j.automatica.2021.109769

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, a distributed diffusion unscented Kalman filtering algorithm based on covariance intersection strategy (DDUKF-CI) is proposed for target tracking with intermittent measurements. By virtue of the pseudo measurement matrix, the standard unscented Kalman filtering (UKF) with intermittent observations is transformed to the information form for the diffusion algorithm to fuse intermediate information from neighbors and improve the estimation performance. Considering unknown correlations in sensor networks, covariance intersection (CI) strategy is combined with the diffusion algorithm. Moreover, it is proved that the proposed DDUKF-CI is consistent and the estimation error is exponentially bounded in mean square using the stochastic stability theory. Finally, the performances of the proposed algorithm and the weighted average consensus unscented Kalman filtering (CUKF) are compared in a target tracking problem with a sensor network. (C) 2021 Elsevier Ltd. All rights reserved.

引用

页数：9

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