Download presentation
Presentation is loading. Please wait.
2
Distributed data fusion in peer-to-peer environment Sergiy Nazarko, InBCT 3.2, Agora center, University of Jyväskylä
3
Data fusion Branch of applied mathematics Combines different pieces of information to receive: – new compatible information – more accurate data
4
Sundial – simple example of data fusion
5
Data fusion applications Military – target tracking – target identification – data association – situation assessment Non-military – machine vision – medical decision support systems – environmental monitoring
6
Multisensor data fusion Improved estimates Problems: – corrupt data – different data – different level of precision – conflicting data
7
Area of interest Data fusion algorithms which can be used for target tracking and identification –Transferable Belief Model –Kalman Filtering
8
“Eye Of Ra” User Interface TBM Kalman Filter
9
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
10
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
11
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”
12
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
13
Interaction between nodes
14
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.
15
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
16
Thank you!
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.