Target Tracking Short

7/27/2019 Target Tracking Short

1/32

Target Tracking


2/32

Introduction

Sittler, in 1964, gave a formal descriptionof the multiple-target tracking (MTT)problem [17].

Traditional target tracking systemsare based on powerful sensor nodes,capable of detecting and locatingtargets in a large range.

Nowadays, tracking methods uselarge-scale wireless sensor networks.


3/32

Introduction

Multiple-Target Tracking (MTT):Varying number of targets arise in the field atrandom locations and at random times.

The movement of each target follows anarbitrary but continuous path, and it persists fora random amount of time before disappearing inthe 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.


4/32

Introduction

Large-scale target tracking wirelessmultisensor system has severaladvantages:

(1) Better geometric fidelity;

(2) Quick deployment

(3) Robustness and accuracy


5/32

Challenges and Difficulties

1. Collaborative communicationand computation

2. Limited processing power3. Tight budget on energy source


6/32

Two Components for Target

Tracking

1.The method that determines thecurrent location of the target. Itinvolves localization as well as the

tracing of the path that the movingtarget takes.

2.Algorithms and network protocols thatenable collaborative informationprocessing among multiple sensornodes.


7/32


8/32

Information-driven dynamic sensor collaboration


9/32


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.


10/32



11/32



12/32



13/32


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.


14/32

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.


15/32

Centralized Tracking Using Binary

Sensors J. Aslam, Z. Butler, V. Crespi, G. Cybenko, and

D. Rus, Tracking a moving object with a binarysensor network, Proc. ACM Int. Conf. EmbeddedNetworked Sensor Systems (SenSys), 2003.

Each sensor node detects one bit of information,namely, whether an object is approaching ormoving away from it. This bit is forwarded to thebasestation along with the node id.

Each sensor performs a detection. If theprobability of presence is greater than theprobability of absence, also called the likelihoodratio, the detection result is positive.


16/32

Centralized Tracking Using Binary

Sensors


17/32

Distributed Tracking Using Binary

Sensors K. Mechitov, S. Sundresh, Y. Kwon, and G.

Agha, Cooperative Tracing with Binary-DetectionSensor 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 tooverlap for this algorithm to work

Sensors should be capable of differentiating thetarget from the environment.


18/32


Sensors Sensors determine whether the object is within

their detection range.

Assuming that sensors are uniformly distributed inthe environment, a sensor with range R will

(1) always detect an object at a distance of lessthan or equal to (R - e) from it,(2) sometimes detect objects that lie at a distanceranging 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.


19/32


Sensors

For each point in time, the objectsestimated position is computed as aweighted average of the detecting nodelocations.

The object path is predicted byextrapolating the target trajectory toenable asynchronous wakeup of nodes

along that path.


20/32


Sensors Different weighting schemes:

1.Assigning equal weights to all readings.

2.Heuristic: wi = ln(1+ ti), where ti is the durationfor which the sensor heard the object.


21/32


Sensors The first scheme yields the most imprecise

results, namely, a higher rate of error betweenactual target path and its sensed path.

The second scheme has a lower error rate and

gives a better approximation of the objecttrajectory.

The third scheme is the most precise method butrequires estimation of the velocity of the object,

which is too costly in terms of the communicationcosts required to make the estimate.

Hence the second approach is the mostappropriate.

Power Conservation and Quality of


22/32


Surveillance in

Target Tracking Sensor Networks C. Gui and P. Mohapatra, Power conservation and quality of

surveillance in target tracking sensor networks, Proc. ACMMobiCom Conf., 2004.

The paper discuss the sleepawake pattern of

each node during the tracking to obtain powerefficiency.

The network operations have two stages:

1. the surveillance stage during the absence of any

event of interest2. the tracking stage, which is in response to any

moving targets.



23/32


Surveillance in

Target Tracking Sensor Networks From a sensor nodes perspective, it should

initially work in the low-power mode when thereare no targets in its proximity.

However, it should exit the low-power mode andbe active continuously for a certain amount oftime when a target enters its sensing range, ormore optimally, when a target is about to enterwithin a short period of time.

Finally, when the target passes by and movesfarther away, the node should decide to switchback to the low-power mode.



24/32


Surveillance in

Target Tracking Sensor Networks Intuitively, a sensor node should enter the tracking mode

and remain active when it senses a target during awakeup period.

However, it is possible that a nodes sensing range ispassed by a target during its sleep period, so that thetarget can pass across a sensor node without beingdetected by the node.

Thus, it is necessary that each node be proactivelyinformed when a target is moving toward it.



25/32


Surveillance in

Target Tracking Sensor Networks Proactive wakeup (PW) algorithm:

Each sensor node has four working modes:

1.waiting

2.prepare3.subtrack

4.tracking

The waiting mode represents the low powermode in surveillance stage. Prepare andsubtrack modes both belong to the preparingand anticipating mode, and a node shouldremain active in both modes.



26/32


Surveillance in

Target Tracking Sensor Networks

Layered onion-like node state distribution around the target.



27/32


Surveillance in

Target Tracking Sensor Networks At any given time, if we draw a circle centered at the

current location of the target where radius r is theaverage sensing range, any node that lies within thiscircle should be in tracking mode.

It actively participates a collaborative tracking operationalong with other nodes in the circle. Regardless of thetracking protocol, the tracking nodes form aspatiotemporal local group, and tracking protocolpackets are exchanged among the group members.

Let us mark these tracking packets so that any node thatis awake within the transmission range can overhear andidentify these packets. Thus, if any node receivestracking packets but cannot sense any target, it shouldbe aware that a target may be coming in the near future.



28/32


Surveillance in

Target Tracking Sensor Networks From the overheard packets, it may also get an

estimation of the current location and moving speedvector of the target.

The node thus transits into the subtrack mode fromeither 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 shouldtransmit a prepare packet. Any node that receives a

prepare packet should transit into prepare mode fromwaiting mode.

A prepare node can be as far as r + 2R away from thetarget.



29/32


Surveillance in

Target 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 receiveany tracking packet, it transits into the prepare mode.

Finally, if it confirms that it can receive neither trackingnor 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 fartheraway from it.

In essence, the PW algorithm makes sure that thetracking group is moving along with the target.


30/32

REFERENCES

1. J. Aslam, Z. Butler, V. Crespi, G. Cybenko, and D. Rus, Tracking a moving objectwith a binary sensor network, Proc. ACM Int. Conf. Embedded NetworkedSensor Systems (SenSys), 2003.

2. Y. Bar-Shalom and X.-R. Li, Multitarget-Multisensor Tarcking: Principles andTechniques, Artech House, 1995.

3. R. R. Brooks, P. Ramanathan, and A. M. Sayeed, Distributed target classi.cation

and tracking in sensor network, Proc. IEEE, 91(8) (2003).4. K. Chakrabarty, S. S. Iyengar, H. Qi, and E. Cho, Grid coverage for surveillanceand target location in distributed sensor networks, IEEE Trans. Comput. 51(12)(2002).

5. C. Y. Chong, K. C. Chang, and S. Mori, Distributed tracking in distributed sensornetworks, Proc. American Control Conf., 1986.

6. M. Chu, H. Haussecker, and F. Zhao, Scalable information-driven sensor

querying and routing for ad hoc heterogeneous sensor networks, Int. J. HighPerform. Comput. Appl. 16(3) (2002).

7. C. Gui and P. Mohapatra, Power conservation and quality of surveillance in targettracking sensor networks, Proc. ACM MobiCom Conf., 2004.

8. R. Gupta and S. R. Das, Tracking moving targets in a smart sensor network, ProcVTC Symp., 2003.


31/32

REFERENCES9. C. F. Huang and Y. C. Tseng, The coverage problem in a wireless sensor network,

Proc. ACM Workshop on Wireless Sensor Networks and Applications (WSNA), 2003.10. M. G. Karpovsky, K. Chakrabaty, and L. B. Levitin, A new class of codes for covering

vertices in graphs, IEEE Trans. Inform. Theory 44 (March 1998).

11. J. Liu, M. Chu, J. Liu, J. Reich, and F. Zhao, Distributed state representation fortracking problems in sensor networks, Proc. 3rd Int. Symp. Information Processing inSensor Networks (IPSN), 2004.

12. J. Liu, J. Liu, J. Reich, P. Cheung, and F. Zhao, Distributed group management for

track initiation and maintenance in target localization applications, Proc. Int.Workshop on Information Processing in Sensor Networks (IPSN), 2003.

13. K. Mechitov, S. Sundresh, Y. Kwon, and G. Agha, Cooperative Tracing with Binary-Detection Sensor Networks, Technical report UIUCDCS-R-2003-2379, ComputerScience Dept., Univ. Illinois at Urbaba Champaign, 2003.

14. L. Y. Pao, Measurement reconstruction approach for distributed multisensor fusion, J.Guid. Control Dynam. (1996).

15. L. Y. Pao and M. K. Kalandros, Algorithms for a class of distributed architecture

tracking, Proc. American Control Conf., 1997.16. N. S. V. Rao, Computational complexity issues in operative diagnosis of graph based

systems, IEEE Trans. Comput. 42(4) (April 1993).


32/32

Date post:	02-Apr-2018
Category:	Documents
Upload:	vivek-singh
View:	221 times
Download:	0 times

Target Tracking Short

Documents