Reaching Consensus with Imprecise Probabilities Over a Network
Name
How_ReachingConsensus.pdf
Size
515.95 KB
Format
Adobe PDF
Checksum (MD5)
40a4b94e3ec7f75f7069d0939b9a353b
Author(s)
How, Jonathan P.
Bertuccelli, Luca F.
Fraser, Cameron S.
Date Issued
August 2009
Journal
AIAA Guidance, Navigation, and Control Conference, Session 11- GNC-7, Networked Unmanned Vehicles
Publisher
American Institute of Aeronautics and Astronautics
Citation
Fraser, Cameron S. R., Luca F. Bertucelli and Jonathan P. How. "Reaching Consensus with Imprecise Probabilities Over a Network." in AIAA Guidance, Navigation, and Control Conference, Chicago, Illinois, Aug. 10-13, 2009.
Version
Original manuscript
Abstract
Information consensus in sensor networks has received much attention due to its numerous applications in distributed decision making. This paper discusses the problem of a distributed group of agents coming to agreement on a probability vector over a network, such as would be required in a decentralized estimation of state transition probabilities or agreement on a probabilistic search map. Unique from other recent consensus literature, however, the agents in this problem must reach agreement while accounting for the uncertainties in their respective probabilities, which are formulated according to generally non-Gaussian distributions. The first part of this paper considers the problem in which the agents seek agreement to the centralized Bayesian estimate of the probabilities, which is accomplished using consensus on hyperparameters. The second part shows that the new hyperparameter consensus methodology can ensure convergence to the centralized estimate even while measurements of a static process are occurring concurrently with the consensus algorithm. A machine repair example is used to illustrate the advantages of hyperparameter consensus over conventional consensus approaches.
Description
http://www.aiaa.org/agenda.cfm?lumeetingid=1998
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Attribution-Noncommercial-Share Alike 3.0 Unported
Persistent DSpace Link
