Target Tracking

1. Target Tracking

2. Introduction • Sittler, in 1964, gave a formal description of the multiple-target tracking (MTT) problem [17]. • Traditional target tracking systems are based on powerful sensor nodes, capable of detecting and locating targets in a large range. • Nowadays, tracking methods use large-scale wireless sensor networks.

3. Introduction • Multiple-Target Tracking (MTT):Varying number of targets arise in the field at random locations and at random times. • The movement of each target follows an arbitrary but continuous path, and it persists for a random amount of time before disappearing in the field. • The target locations are sampled at random intervals. • The goal of the MTT problem is to find the moving path for each target in the field.

4. Introduction • Large-scale target tracking wireless multisensor system has several advantages: (1) Better geometric fidelity; (2) Quick deployment (3) Robustness and accuracy

5. Challenges and Difficulties • Collaborative communication and computation • Limited processing power • Tight budget on energy source

6. Two Components for Target Tracking • The method that determines the current location of the target. It involves localization as well as the tracing of the path that the moving target takes. • Algorithms and network protocols that enable collaborative information processing among multiple sensor nodes.

7. Information-drivendynamic sensor collaboration • F. Zhao, J. Shin, and J. Reich, Information-driven dynamic sensor collaboration for tracking applications, IEEE Signal Proces. Mag. (March 2002). • The participants for collaboration in a sensor network were determined by dynamically optimizing the information utility of data for a given cost of computation and communication. • The metrics used to determine the participant nodes (who should sense and whom the information must be passed to) are(1) detection quality(2) track quality(3) scalability(4) survivability(5) resource usage

8. Information-driven dynamic sensor collaboration

9. Information-driven dynamic sensor collaboration • A user sends a query that enters the sensor network. • Metaknowledge then guides this query toward the region of potential events. • The leader node generates an estimate of the object state and determines the next best sensor based on sensor characteristics. • It then hands off the state information to newly selected leader. • The new leader combines its estimate with the previous estimate to derive a new state, and selects the next leader. • This process of tracking the object continues and periodically the current leader nodes send back state information to the querying node using a shortest-path routing algorithm.

10. Information-driven dynamic sensor collaboration

11. Information-driven dynamic sensor collaboration

12. Information-driven dynamic sensor collaboration

13. Information-driven dynamic sensor collaboration • Summary:The algorithm described is power-efficient in terms of bandwidth. • The selection of sensors is a local decision. Thus, if the first leader is incorrectly elected, it could have a cascading effect and overall accuracy could suffer. • It is also computationally heavy on leader nodes. • This approach is applied to tracking a single object only.

14. Tracking Using Binary Sensors • Binary sensors are so called because they typically detect one bit of information. • This one bit could be used to represent indicate whether the target is(1) within the sensor range or(2) moving away from or toward the sensor.

15. Centralized Tracking Using Binary Sensors • J. Aslam, Z. Butler, V. Crespi, G. Cybenko, and D. Rus, Tracking a moving object with a binary sensor network, Proc. ACM Int. Conf. Embedded Networked Sensor Systems (SenSys), 2003. • Each sensor node detects one bit of information, namely, whether an object is approaching or moving away from it. This bit is forwarded to the basestation along with the node id. • Each sensor performs a detection. If the probability of presence is greater than the probability of absence, also called the likelihood ratio, the detection result is positive.

16. Centralized Tracking Using Binary Sensors

17. Distributed Tracking Using Binary Sensors • K. Mechitov, S. Sundresh, Y. Kwon, and G. Agha, Cooperative Tracing with Binary-Detection Sensor Networks, Technical report UIUCDCS-R-2003-2379, Computer Science Dept., Univ. Illinois at Urbaba — Champaign, 2003. • It is assumed that nodes know their locations and that their clocks are synchronized. • The density of sensor nodes should be high enough for sensing ranges of several sensors to overlap for this algorithm to work • Sensors should be capable of differentiating the target from the environment.

18. Distributed Tracking Using Binary Sensors • Sensors determine whether the object is within their detection range. • Assuming that sensors are uniformly distributed in the environment, a sensor with range R will(1) always detect an object at a distance of less than or equal to (R - e) from it, (2) sometimes detect objects that lie at a distance ranging between (R – e) and (R + e)(3) never detect any object outside the range of (R + e), where e = 0.1R but could be user-defined.

19. Distributed Tracking Using Binary Sensors • For each point in time, the object’s estimated position is computed as a weighted average of the detecting node locations. • The object path is predicted by extrapolating the target trajectory to enable asynchronous wakeup of nodes along that path.

20. Distributed Tracking Using Binary Sensors • Different weighting schemes: • Assigning equal weights to all readings. • Heuristic: wi = ln(1+ ti), where ti is the duration for which the sensor heard the object.

21. Distributed Tracking Using Binary Sensors • The first scheme yields the most imprecise results, namely, a higher rate of error between actual target path and its sensed path. • The second scheme has a lower error rate and gives a better approximation of the object trajectory. • The third scheme is the most precise method but requires estimation of the velocity of the object, which is too costly in terms of the communication costs required to make the estimate. • Hence the second approach is the most appropriate.

22. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks • C. Gui and P. Mohapatra, Power conservation and quality of surveillance in target tracking sensor networks, Proc. ACM MobiCom Conf., 2004. • The paper discuss the sleep–awake pattern of each node during the tracking to obtain power efficiency. • The network operations have two stages: • the surveillance stage during the absence of any event of interest • the tracking stage, which is in response to any moving targets.

23. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks • From a sensor node’s perspective, it should initially work in the low-power mode when there are no targets in its proximity. • However, it should exit the low-power mode and be active continuously for a certain amount of time when a target enters its sensing range, or more optimally, when a target is about to enter within a short period of time. • Finally, when the target passes by and moves farther away, the node should decide to switch back to the low-power mode.

24. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks • Intuitively, a sensor node should enter the tracking mode and remain active when it senses a target during a wakeup period. • However, it is possible that a node’s sensing range is passed by a target during its sleep period, so that the target can pass across a sensor node without being detected by the node. • Thus, it is necessary that each node be proactively informed when a target is moving toward it.

25. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks • Proactive wakeup (PW) algorithm: • Each sensor node has four working modes: • waiting • prepare • subtrack • tracking • The waiting mode represents the low power mode in surveillance stage. Prepare and subtrack modes both belong to the preparing and anticipating mode, and a node should remain active in both modes.

26. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks Layered onion-like node state distribution around the target.

27. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks • At any given time, if we draw a circle centered at the current location of the target where radius r is the average sensing range, any node that lies within this circle should be in tracking mode. • It actively participates a collaborative tracking operation along with other nodes in the circle. Regardless of the tracking protocol, the tracking nodes form a spatiotemporal local group, and tracking protocol packets are exchanged among the group members. • Let us mark these tracking packets so that any node that is awake within the transmission range can overhear and identify these packets. Thus, if any node receives tracking packets but cannot sense any target, it should be aware that a target may be coming in the near future.

28. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks • From the overheard packets, it may also get an estimation of the current location and moving speed vector of the target. • The node thus transits into the subtrack mode from either waiting mode or prepare mode. At the boundary, ap subtrack node can be r + R away from the target, where R is the transmission range. • To carry the wakeup wave farther away, a node should transmit a prepare packet. Any node that receives a prepare packet should transit into prepare mode from waiting mode. • A prepare node can be as far as r + 2R away from the target.

29. Power Conservation and Quality of Surveillance inTarget Tracking Sensor Networks • If a tracking node confirms that it can no longer sense the target, it transits into the subtrack mode. • Further, if it later confirms that it can no longer receive any tracking packet, it transits into the prepare mode. • Finally, if it confirms that it can receive neither tracking nor prepare packet, it transits back into the waiting mode. • Thus, a tracking node gradually turns back into low-power surveillance stage when the target moves farther away from it. • In essence, the PW algorithm makes sure that the tracking group is moving along with the target.

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