NDLI: Decentralized filtering a multi-agent system with local parametric couplings based on Kalman filter

Content Provider	IEEE Xplore Digital Library
Author	Yini Lv Hongbin Ma Mengyin Fu Chenguang Yang
Copyright Year	2013
Description	Author affiliation: Key Lab. of Intell. Control & Decision of Complex Syst., Beijing Instn. of Technol., Beijing, China (Yini Lv; Hongbin Ma; Mengyin Fu) \|\| Sch. of Comput. & Math., Plymouth Univ., Plymouth, UK (Chenguang Yang)
Abstract	In this paper, decentralized filtering of multiagent systems with coupling uncertainties is proposed and investigated. The considered multi-agent system is composed of many agents, each of which evolves with a discrete-time stochastic linear time-varying dynamics, and every agent can be locally influenced by its neighbor agents. Therefore the states evolution of each agent is not only related with its previous states but also related with its neighbors' previous states in the linear dynamic system. Communication limitations existing in the considered multi-agent system restrict that each agent can only observe its own measurements (outputs) and its neighbor agents' outputs while the states are invisible to any agent. Because of communication limitations and information constraints, without knowing the coupling gains of the local interactions, it is not easy for each agent to estimate its states by traditional kalman filter or other state observers, which were extensively discussed in the literature. In this preliminary study, for the considered coupled linear discrete-time multiagent system with uncertain linear local couplings, based on the key idea of state augmentation and the certainty-equivalence principle borrowed from the area of adaptive control, we propose an efficient decentralized kalman filtering scheme, for each agent, to simultaneously estimate the unknown states and coupling parameters, and extensive simulations are conducted, which have clearly verified the effectiveness of the proposed decentralized filtering scheme.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	101
Ending Page	106
File Size	189666
Page Count	6
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6560902
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	parametric couplings Noise Estimation decentralized filtering kalman filter multi-agent system Equations Couplings states estimation parameter estimation Kalman filters Mathematical model Multi-agent systems
Content Type	Text
Resource Type	Article

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