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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

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
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
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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