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CLC number: TP13

On-line Access: 2018-09-12

Received: 2017-01-24

Revision Accepted: 2017-03-23

Crosschecked: 2018-07-08

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Citations:  Bibtex RefMan EndNote GB/T7714


Rong-hao Zheng


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.9 P.1063-1075


Convergence analysis of distributed Kalman filtering for relative sensing networks

Author(s):  Che Lin, Rong-hao Zheng, Gang-feng Yan, Shi-yuan Lu

Affiliation(s):  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China ; more

Corresponding email(s):   linche@zju.edu.cn, rzheng@zju.edu.cn, ygf@zju.edu.cn, 917964950@qq.com

Key Words:  Relative sensing network, Distributed Kalman filter, Schur stable, Linear matrix inequality

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Che Lin, Rong-hao Zheng, Gang-feng Yan, Shi-yuan Lu. Convergence analysis of distributed Kalman filtering for relative sensing networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1063-1075.

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author="Che Lin, Rong-hao Zheng, Gang-feng Yan, Shi-yuan Lu",
journal="Frontiers of Information Technology & Electronic Engineering",
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%A Che Lin
%A Rong-hao Zheng
%A Gang-feng Yan
%A Shi-yuan Lu
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 9
%P 1063-1075
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%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700066

T1 - Convergence analysis of distributed Kalman filtering for relative sensing networks
A1 - Che Lin
A1 - Rong-hao Zheng
A1 - Gang-feng Yan
A1 - Shi-yuan Lu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 9
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700066

We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis. The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality (LMI) equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.


摘要:严格分析了相对传感网络中分布式卡尔曼滤波问题。用无向图对相对传感网络建模,并假设网络中各节点具有相同动力学模型。给出整个系统可观测性的充要条件,并详细证明。通过局部信息和测量通信,设计了一种基于卡尔曼滤波技术的分布式次优估计器,并与集中式最优估计器比较。给出系统收敛对应的网络拓扑充分条件,并结合系统参数给出n个线性矩阵不等式(linear matrix inequality, LMI)方程的数值解。数值仿真验证了所提算法的有效性。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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