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Distributed data fusion in peer-to-peer environment

Distributed data fusion in peer-to-peer environment. Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä. Data fusion. Branch of applied mathematics Combines different pieces of information to receive: new compatible information more accurate data.

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Distributed data fusion in peer-to-peer environment

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

  2. Data fusion • Branch of applied mathematics • Combines different pieces of information to receive: • new compatible information • more accurate data

  3. Sundial – simple example of data fusion

  4. Military target tracking target identification data association situation assessment Non-military machine vision medical decision support systems environmental monitoring Data fusion applications

  5. Multisensor data fusion • Improved estimates • Problems: • corrupt data • different data • different level of precision • conflicting data

  6. Area of interest • Data fusion algorithms which can be used for target tracking and identification • Transferable Belief Model • Kalman Filtering

  7. “Eye Of Ra” • User Interface • TBM • Kalman Filter

  8. Decentralized data fusion systems • Collection of processing nodes • None of the nodes has knowledge about the overall network topology • Each node performs a specific computing task • No central node exists that controls the network

  9. Features of DDFSs • Reliability • no central node • loss of nodes or links does not prevent rest of the system from functioning • Flexibility • nodes can be added or deleted by making only local changes • only establishment of links to one or more nodes is needed

  10. Work 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”

  11. Work in process • Integration 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

  12. Interaction between nodes

  13. Network components • -little Square – sensor node with transmission capabilities • - bold square –control node with sensor’s node capabilities • GUI – user interface which displays tracking trajectory.

  14. Future work • Further learning of data fusion methods • Fusion of TBM and Kalman filter • Implementing totally distributed data fusion system based on peer-to-peer platform • Evaluation and research

  15. Thank you!

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