JYVÄSKYLÄN YLIOPISTO 2003 InBCT 3.2 M.Sc. Sergiy Nazarko Cheese Factory –project Distributed Data Fusion In Peer2Peer Environment

JYVÄSKYLÄN YLIOPISTO 2003 Area of interests  Data fusion algorithms which can be used for target tracking and identification –Kalman Filtering –Transferable belief model

JYVÄSKYLÄN YLIOPISTO 2003 Distributed data fusion  Real World Simulator (trajectory generator) –Creates scenario of target movements –Feeds sensors with data via XML- files  Situation display –Shows the real trajectory of target –Measured trajectory –Estimated trajectory –Predicted trajectory  P2PStudio –Generates network failures Real-World Simulator P2P Studio Situation display

JYVÄSKYLÄN YLIOPISTO 2003 Track Simulator  Observation space temporarily divided into 6 sectors  Every sector may contain any number of sensor nodes  Possibility to create scenarios of target movements  Generated data is stored in XML- files

JYVÄSKYLÄN YLIOPISTO 2003 Example of data for one sensor

JYVÄSKYLÄN YLIOPISTO 2003 Distributed Data Fusion  Situation display is impossible without Data Fusion  Collects information from the sensor nodes and combines them to obtain clear picture of the whole observation space –Classifies the observation data and uses this to make joint estimation between different sensors –Works as request-reply application

JYVÄSKYLÄN YLIOPISTO 2003 Situation display  Now situation display is combined with data fusion in one node –Target trajectory is displayed dynamically, when the Kalman filter computation gives new set of points  Displays various types of trajectory –Real trajectory (stored in XML files) –Measured trajectory (given by sensors) sensor data is corrupted by noise –Estimated trajectory (Kalman Filter) –Predicted trajectory (Kalman Filter)

JYVÄSKYLÄN YLIOPISTO 2003 System Description  External Real world simulator  One Chedar-application – different features –Sensor node –Display&Data fusion node  Possibility to run simultaneously few Display&Data fusion nodes to protect system from the failure of one of them  At any time any Sensor node can become processing node by simple clicking on the button  Tracking application based on Chedar and Real world simulator have been developed

JYVÄSKYLÄN YLIOPISTO 2003 System Weaknesses  Request-reply architecture –Every new measurement is requested by flooding the whole network with resource request (in real world sensor should send coordinates when they are received) –System works not in real time but it has it’s own time (timestamp), always starting from one  If only one situation display node is running, and it crashes, new situation display node will start with initial parameters of Kalman filter –Need time for filter adjusting

JYVÄSKYLÄN YLIOPISTO 2003 Lea Pulkkinen’s hall Classroom Current configuration  Classroom with workstations running 12 Chedar nodes, which act as Sensor nodes  Workstation running Situation display  Trajectory is already generated SSSSSSSSSSSS D

JYVÄSKYLÄN YLIOPISTO 2003 Future Work  Add one or more targets into simulation –Transferable belief model will appear on scene  Make distributed processing network –Separate Data Fusion node from Situation Display node –Put few processing nodes in every sector –Store backup copies of Kalman filter parameters in other nodes –Make information about target follow the target  Discover quality of measurements by destroying sensor node –Find how quality of measurements depends on number of sensors –What is the optimal quantity of sensors in one sector?

JYVÄSKYLÄN YLIOPISTO 2003 Future Work (continued)  Improve request-reply architecture –Smart sensor should send information about target whenever the information is available –Data fusion node sends request periodically (e.g. every 30 seconds), in case new sensor appears on the scene  Create a sensor network that measures a real environment –E.g. 10 microcontrollers with infrared sensing chip and a radio interface chip, distributed all over Agora –1 or more moving targets :-)

JYVÄSKYLÄN YLIOPISTO 2003 Thank You!