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!