Asymptotically Optimal Distributed Filtering of Continuous-Time Linear Systems. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Battilotti, S.; Cacace, F.; d'Angelo, M.; Germani, A.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 3242
• Page End: 3247
• Publication Date: 2020
• Subjects: Continuous time filters -- Kalman filters -- Filtering theory -- Consensus filters; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1124
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.