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

• SummaryThe 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 objects 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 sleepawake 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 nodes 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 nodes 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.

30
REFERENCES

• 1. 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.
• 2. Y. Bar-Shalom and X.-R. Li, Multitarget-Multise
  nsor Tarcking Principles and Techniques, 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 surveillance and 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 sensor
  networks, 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.
  High Perform. Comput. Appl. 16(3) (2002).
• 7. C. Gui and P. Mohapatra, Power conservation
  and quality of surveillance in target tracking
  sensor networks, Proc. ACM MobiCom Conf., 2004.
• 8. R. Gupta and S. R. Das, Tracking moving
  targets in a smart sensor network, Proc VTC
  Symp., 2003.

31
REFERENCES

• 9. 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 for
  tracking problems in sensor networks, Proc. 3rd
  Int. Symp. Information Processing in Sensor
  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, Computer Science 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
REFERENCES

• 17. R. W. Sittler, An optimal data association
  problem in surveillance theory, IEEE Trans.
  Military Electron. (April 1964).
• 18. Q. X.Wang,W. P. Chen, R. Zheng, K. Lee, and
  L. Sha, Acoustic target tracking using tiny
  wireless sensor devices, Proc. Int. Workshop on
  Information Processing in Sensor Networks (IPSN),
  2003.
• 19. Y. Xu, J. Heidemann, and D. Estrin, Geography
  informed energy conservation for ad hoc routing,
  Proc. ACM MobiCom Conf., 2001.
• 20. L. Yuan, C. Gui, C. Chuah, and P. Mohapatra,
  Applications and design of hierarchical and/or
  broadband sensor networks, Proc. BASENETS Conf.,
  2004.
• 21. W. Zhang and G. Cao, Dctc Dynamic convoy
  tree-based collaboration for target tracking in
  sensor networks, IEEE Trans. Wireless Commun.
  11(5) (Sept. 2004).
• 22. W. Zhang and G. Cao, Optimizing tree
  recon.guration for mobile target tracking in
  sensor networks, Proc. IEEE InfoCom., 2004.
• 23. W. Zhang, J. Hou, and L. Sha, Dynamic
  clustering for acoustic target tracking in
  wireless sensor networks, Proc. 11th IEEE Int.
  Conf. Network Protocols (ICNP), 2003.
• 24. F. Zhao, J. Shin, and J. Reich,
  Information-driven dynamic sensor collaboration
  for tracking applications, IEEE Signal Proces.
  Mag. (March 2002).
• 25. Y. Zhou and K. Chakrabarty, Sensor deployment
  and target localization in distributed sensor
  networks, ACM Trans. Embedded Comput. Syst. 3
  (Feb. 2004).