Distributed Nonlinear Consensus in the Space of Probability Measures

Select |


Bishop, Adrian; Doucet, Arnaud


Conference Material

19th World Congress of the International Federation of Automatic Control

Cape Town, South Africa


Distributed consensus in the Wasserstein metric space of probability measures is introduced for the first time in this paper. It is shown that convergence of the individual agents’ measures to a common measure value is guaranteed as long as a very weak network connectivity condition is satisfied asymptotically. The common measure value that is achieved asymptotically at each agent is the one that is closest simultaneously to all initial agent measures in the sense that it will minimise some weighted sum of Wasserstein distances between it and all initial agent measures. This algorithm has wide applicability in the field of distributed estimation and distributed information fusion.



Bishop, Adrian; Doucet, Arnaud. Distributed Nonlinear Consensus in the Space of Probability Measures.[Conference Material]. 2014-08-24. <a href="http://hdl.handle.net/102.100.100/94345?index=1" target="_blank">http://hdl.handle.net/102.100.100/94345?index=1</a>

Loading citation data...

Citation counts
(Requires subscription to view)