Electrical Engineering and Systems Science > Systems and Control
[Submitted on 12 Sep 2020 (v1), last revised 17 Feb 2021 (this version, v3)]
Title:Distributed Kalman Estimation with Decoupled Local Filters
View PDFAbstract:We study a distributed Kalman filtering problem in which a number of nodes cooperate without central coordination to estimate a common state based on local measurements and data received from neighbors. This is typically done by running a local filter at each node using information obtained through some procedure for fusing data across the network. A common problem with existing methods is that the outcome of local filters at each time step depends on the data fused at the previous step. We propose an alternative approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. The main feature of the new approach is that fusion errors introduced at a given time step do not carry over to subsequent steps. This offers advantages in many situations including when a global estimate in only needed at a rate slower than that of measurements or when there are network interruptions. If the global structural data can be fused correctly asymptotically, the stability of local filters is equivalent to that of the centralized Kalman filter. Otherwise, we provide conditions to guarantee stability and bound the resulting estimation error. Numerical experiments are given to show the advantage of our method over other existing alternatives.
Submission history
From: Damian Marelli [view email][v1] Sat, 12 Sep 2020 14:07:12 UTC (54 KB)
[v2] Sun, 29 Nov 2020 11:55:31 UTC (54 KB)
[v3] Wed, 17 Feb 2021 11:57:13 UTC (54 KB)
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