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Distributed data Distributed data fusion in peer-to-peer fusion in peer-to-peer environment environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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Page 1: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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

Page 2: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Data fusionData fusion

Branch of applied mathematicsCombines different pieces of information to

receive:– new compatible information– more accurate data

Page 3: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Sundial – simple example of Sundial – simple example of data fusiondata fusion

Page 4: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Data fusion applicationsData fusion applications

Military– target tracking– target identification– data association– situation assessment

Non-military– machine vision– medical decision

support systems– environmental

monitoring

Page 5: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Multisensor data fusionMultisensor data fusion

Improved estimatesProblems:

– corrupt data– different data– different level of precision– conflicting data

Page 6: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Area of interestArea of interestData fusion algorithms which can be

used for target tracking and identification– Transferable Belief Model– Kalman Filtering

Page 7: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

““Eye Of Ra”Eye Of Ra”

User InterfaceTBM Kalman Filter

Page 8: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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

Page 9: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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

Page 10: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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”

Page 11: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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

Page 12: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Interaction between nodesInteraction between nodes

Page 13: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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.

Page 14: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

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

Page 15: Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä

Thank you!Thank you!