distributed data fusion in peer-to-peer environment sergiy nazarko, inbct 3.2, agora center,...
Post on 21-Dec-2015
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![Page 1: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä](https://reader035.vdocuments.mx/reader035/viewer/2022062714/56649d585503460f94a372f8/html5/thumbnails/1.jpg)
Distributed data fusion in Distributed data fusion in peer-to-peer environmentpeer-to-peer environment
Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä
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Data fusionData fusion
Branch of applied mathematicsCombines different pieces of information to
receive:– new compatible information– more accurate data
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Sundial – simple example of Sundial – simple example of data fusiondata fusion
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Data fusion applicationsData fusion applications
Military– target tracking– target identification– data association– situation assessment
Non-military– machine vision– medical decision
support systems– environmental
monitoring
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Multisensor data fusionMultisensor data fusion
Improved estimatesProblems:
– corrupt data– different data– different level of precision– conflicting data
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Area of interestArea of interestData fusion algorithms which can be
used for target tracking and identification– Transferable Belief Model– Kalman Filtering
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““Eye Of Ra”Eye Of Ra”
User InterfaceTBM Kalman Filter
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Decentralized data fusion Decentralized data fusion systemssystems
Collection of processing nodesNone of the nodes has knowledge about the
overall network topologyEach node performs a specific computing
taskNo central node exists that controls the
network
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Features of DDFSsFeatures of DDFSs
Reliability– no central node– loss of nodes or links does not prevent rest of
the system from functioningFlexibility
– nodes can be added or deleted by making only local changes
– only establishment of links to one or more nodes is needed
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Work doneWork done
Master’s thesises: – S. Nazarko, Evaluation of Data Fusion Methods Using
Kalman Filter and TBM– V. Smirnova, Multiagent System for Distributed Data
Fusion in Peer-to-Peer Environment
Gained experience in applying data fusion methods
“Eye Of Ra”
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Work in processWork in processIntegration of evaluated algorithm into Chedar
– To get a little bit clearer picture on this step only Kalman filter will be implemented as part of Chedar
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Interaction between nodesInteraction between nodes
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Network componentsNetwork components
-little Square – sensor node with transmission capabilities
- bold square –control node with sensor’s node capabilities
GUI – user interface which displays tracking trajectory.
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Future workFuture work
Further learning of data fusion methodsFusion of TBM and Kalman filterImplementing totally distributed data fusion
system based on peer-to-peer platformEvaluation and research
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Thank you!Thank you!