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2020, 21th World Congress of the International-Federation-of-Automatic-Control (IFAC), Pages -

Asymptotically Optimal Distributed Filtering of Continuous-Time Linear Systems (04b Atto di convegno in volume)

Battilotti S., Cacace F., d'Angelo M., Germani A.

In this paper we prove the following new and unexpected result: it is possible to design a continuous-time distributed filter for linear systems that asymptotically tends at each node to the optimal centralized filter. The result concerns distributed estimation over a connected undirected graph and it only requires to exchange the estimates among adjacent nodes. We exhibit an algorithm containing a consensus term with a parametrized gain and show that when the parameter becomes arbitrarily large the error covariance at each node becomes arbitrarily close to the error covariance of the optimal centralized Kalman filter.
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