Distributed data fusion in peer to peer environment
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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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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

Data fusion

  • Branch of applied mathematics

  • Combines different pieces of information to receive:

    • new compatible information

    • more accurate data


Sundial simple example of data fusion

Sundial – simple example of data fusion


Data fusion applications

Military

target tracking

target identification

data association

situation assessment

Non-military

machine vision

medical decision support systems

environmental monitoring

Data fusion applications


Multisensor data fusion

Multisensor data fusion

  • Improved estimates

  • Problems:

    • corrupt data

    • different data

    • different level of precision

    • conflicting data


Area of interest

Area of interest

  • Data fusion algorithms which can be used for target tracking and identification

    • Transferable Belief Model

    • Kalman Filtering


Eye of ra

“Eye Of Ra”

  • User Interface

  • TBM

  • Kalman Filter


Decentralized data fusion systems

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


Features of ddfss

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


Work done

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”


Work in process

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


Interaction between nodes

Interaction between nodes


Network components

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.


Future work

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


Thank you

Thank you!

EUROOPAN UNIONI


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