Multiple-target Data

1. Presented by: Chaitanya K. SambharaPaper by: Maarten Ditzel, Caspar Lageweg, Johan Janssen, Arne Theil TNO Defence, Security and Safety, The Hague, The Netherland Multiple-target Data Aggregation and Tracking in Wireless Sensor Networks

2. Introduction • We discuss the problem of target tracking. • Target Tracking : • Current Position • Estimated Position.

3. Aim • To study the tracking of several objects in a sensor network simultaneously and focus on trade-offs between the amount of communication in the network and tracking accuracy.

4. Main Goal • The main goal is to investigate various approaches to reduce the number of required messages while achieving a certain track accuracy.

5. What is the target ? • The target can be a moving vehicle, for example, or can be a phenomenon such as an approaching fire. • It is assumed that each individual sensor node is equipped with appropriate sensory device(s) to be able to detect the target as well as to estimate its distance based on the sensed data.

6. How it works • The sensors that are triggered by the target collaborate to localize the target in the physical space to predict its course.

7. Assumptions • Sensor nodes are scattered randomly in a geographical region. Each node is aware of its location. • Absolute location information is not needed. It is sufficient for the nodes to know their location with respect to a common reference point.

8. Assumptions for location lnformation • In their experiment the sensor nodes are stationary and we have directly encoded the location information into the sensor nodes to eliminate the possibility of any localization error. • Hence there is no emphasis on any particular localization technique.

9. Assumptions • The sensors must be capable of estimating the distance of the target to be tracked from the sensor readings. • It is assumed that the sensor has already learned the sensor reading to distance mapping.

10. Tracking a target • Tracking a target involves three distinct steps: • Detecting the presence of the target. • Determining the direction of motion of the target. • Alerting appropriate nodes in the network.

11. Issues • Communication of data consumes more energy than processing. • Reduction of amount of messages sent can be achieved by utilizing local processing.

12. Data Aggregation • In data aggregation local sensor readings are combined to reduce the communication load of the network. • This decreases the communication cost by a considerable amount.

13. Aggregation Strategies • 1. Reference • 2. Differential Messaging • 3. Local Aggregation • 4. Local Aggregation and Differential Messaging

14. Aggregation Strategies • #2. Differential Messaging • Uses local processing • When the previously sensed target moves, the observed quality changes. • Threshold used for quality difference is ▲T

15. Aggregation Strategies • #3. • Data aggregation used. • Quality observation(normalized strength of the sensor signal) used by a node among the group.

16. Aggregation Strategies • #4. • Uses both, differential messaging and local aggregation

17. Position Estimation • Quality of the observation is qiε[0, 1] • Each node i observing the target is assumed to broadcast {xi, qi(t)}, • Where

18. Position Estimation • The weight wi(t) of • each observation is measured as • The estimated position is

19. Simulation Environment • Network Topology: • Assumed that the wireless sensor network consists of a 51×51 grid of sensor nodes, placed 100 m apart. • The communication range rc of the network is chosen such that nodes can only communicate with their direct neighbors. • The sink is located at the center of the network.

20. Target Trajectory Path

21. is given by:

22. Algorithms • The target estimation algorithm is executed centrally at the sink in simulations 1 and 2. • In the simulations 3 and 4 it is run locally at the nodes. • The track algorithm is assumed to run at the sink node.

23. Tracking • To determine a target’s trajectory a Kalman filter is used. • The Kalman filter is a recursive filter, which estimates the state of a dynamic system from a series of noisy measurements.

24. Multi Stage Contact-Track Association • False contacts can result in false tracks. To reduce the number of false tracks, multi-stage contact-track association is applied. • The technique labels tracks in three categories: potential, tentative and confirmed.

25. Tracker

26. Results

27. Target Tracking Results

28. Strategy 1

29. Strategy 2

30. Strategy 3

31. Strategy 4

32. Conclusion • We discussed different aggregation strategies and analyzed them. • The results show a relevant trade-off between the amount of communication and the performance of the track algorithm.

33. Conclusion • From the results, the local aggregation without differential messaging (Strategy #3) shows better cost-performance trade-off in noisy environments.

34. Thank You

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