Distributed State Estimation for Dynamic Systems

A grow of locally available computing power, coupled with increasingly powerful and flexible communication structures, enables the use of decentralized or distributed processing concept nowadays. This concept makes the inference of the hidden state of complex and geographically widely distributed dynamic systems much more stable and flexible compared to centralized processing. Possible applications are such as distributed diffusion field estimation using wireless sensor networks, distributed cyber-physical systems related to industry 4.0, distributed target tracking or localization and distributed autonomous spacecraft formation control. To estimate the state of a dynamic system in a distributed manner and optimally adapt to dynamic properties of the physical processes, a new design of distributed Kalman filtering and its extension should be investigated in order to obtain a comparable robust and stable estimation result meanwhile considering communication constraints.

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Duration: since 09/2016
Funding:ANT

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Last change on 29.08.2019 by S. Wang
AIT ieee tzi ith Fachbereich 1
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