CLC number: TP13
On-line Access: 2018-09-12
Received: 2017-01-24
Revision Accepted: 2017-03-23
Crosschecked: 2018-07-08
Cited: 0
Clicked: 6218
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.
@article{title="Convergence analysis of distributed Kalman filtering for relative sensing networks",
author="Che Lin, Rong-hao Zheng, Gang-feng Yan, Shi-yuan Lu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="9",
pages="1063-1075",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700066"
}
%0 Journal Article
%T Convergence analysis of distributed Kalman filtering for relative sensing networks
%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
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700066
TY - JOUR
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
SP - 1063
EP - 1075
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700066
Abstract: 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.
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