Skip to main content
SpringerLink
Log in

Optimization of Multi-target Tracking Within a Sensor Network Via Information Guided Clustering

  • Chapter
  • First Online:
Handbook of Dynamic Data Driven Applications Systems

  • 1306 Accesses

Abstract

This work presents a new algorithm for rapid and efficient clustering of sensing nodes within a heterogeneous wireless sensor network. The objective is to enable optimal sensor allocation for localization uncertainty reduction in multi-target tracking. The proposed algorithm is built on three metrics: (i) sensing feasibility; (ii) measurement quality to maximize information utility; and, (iii) communication cost to minimize data routing time. The derived cluster is employed as the search-space for optimal sensor allocation via maximizing the uncertainty reduction of the expected probability distribution over a target’s state-space. Theoretical analysis is used to show advantage of the proposed method in terms of information utility over the widely used Euclidean distance based clustering approach. The analysis is verified via simulated target tracking examples, in terms of metrics of information utility and computational expenditure. Simulations also reveal relationships between sensor field density and the extent of information gain over competing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The use of the term differs throughout the literature. In this study, “clustering” only pertains the act of reducing the set of potential nodes for further sensor selection.

  2. 2.

    “ECA” is used as an umbrella term for clustering procedures that prioritize chosen sensors’ proximity to predicted target positions. Obviously, not all proximity-based methods are identical and vary depending on the application.

  3. 3.

    For simplicity, the error term r 0 is a positive constant, r 0 << d.

  4. 4.

    This study utilizes an exhaustive search to locate a single optimal sensor, as opposed to a conventional graph neuron set.

References

  1. J.L. Crassidis, J.L. Junkins, Optimal Estimation of Dynamic Systems. Chapman & Hall/CRC Applied Mathematics & Nonlinear Science, 2nd edn. (Chapman & Hall/CRC, Boca Raton, 2011)

    Google Scholar 

  2. J. Liu, J. Reich, F. Zhao, Collaborative in-network processing for target tracking. EURASIP J. Appl. Signal Process. 4, 378–391 (2003). 616720

    Article  Google Scholar 

  3. M.L. Hernandez, T. Kirubarajan, Y. Bar-Shalom, Multisensor resource deployment using posterior Cramer-Rao bounds. IEEE Trans. Aerosp. Electron. Syst. 40, 399–416 (2004)

    Article  Google Scholar 

  4. P.V. Pahalawatta, D. Depalov, T.N. Pappas, A.K. Katsaggelos, Detection, classification, and collaborative tracking of multiple targets using video sensors, in Information Processing in Sensor Networks (Springer, Berlin, 2003), pp. 529–544

    MATH  Google Scholar 

  5. K. Chakrabarty, Y. Zou, Sensor deployment and target localization based on virtual forces, in Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, San Francisco, 2003, pp. 71–75

    Google Scholar 

  6. M. Hernandez R. Tharmarasa, T. Kirubarajan, Large-scale optimal sensor array management for multitarget tracking. IEEE Trans. Syst. Man Cybern. 37(5), 803–814 (2007)

    Article  Google Scholar 

  7. X. Shen, S. Liu, P.K. Varshney, Sensor selection for nonlinear systems in large sensor networks. IEEE Trans. Aeros. Electron. Syst. 50(4), 2664–2678 (2014)

    Article  Google Scholar 

  8. T. Wang, Z. Peng, J. Liang, S. Wen, M.Z.A. Bhuiyan, Y. Cai, J. Cao, Following targets for mobile tracking in wireless sensor networks. ACM Trans. Sensor Netwo. (TOSN) 12(4), 31 (2016)

    Google Scholar 

  9. F.M. Dommermuth, The estimation of target motion parameters from CPA time measurements in a field of acoustic sensors. J. Acoust. Soc. Am. 83(4), 1476–1480 (1988)

    Article  Google Scholar 

  10. Q. Yang, A. Lim, K. Casey, R. Neelisti, Real-time target tracking with CPA algorithm in wireless sensor networks, in 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, San Francisco, 2008, pp. 305–312

    Google Scholar 

  11. T. Ahmed O. Demigha, W. Hidouci, On energy efficiency in collaborative target tracking in wireless sensor network: a review. IEEE Commun. Surv. Tutorials 15(3), 1210–1222 (2013)

    Article  Google Scholar 

  12. A. Cerpa, J. Elson, D. Estrin, L. Girod, M. Hamilton, J. Zhao, Habitat monitoring: application driver for wireless communications technology. SIGCOMM Comput. Commun. Rev. 31(2 supplement), 20–41 (2001)

    Article  Google Scholar 

  13. A. Farina, G. Golino, A. Capponi, C. Pilotto, Surveillance by means of a random sensor network: a heterogeneous sensor approach, in 2005 7th International Conference on Information Fusion, Philadelphia, ed. by E. Blasch, vol. 2, 2005

    Google Scholar 

  14. J. Lin, W. Xiao, F. L. Lewis, L. Xie, Energy-efficient distributed adaptive multisensor scheduling for target tracking in wireless sensor networks. IEEE Trans. Instrum. Meas. 58(6), 1886–1896 (2009)

    Article  Google Scholar 

  15. B. Sikdar, H. Yang, A protocol for tracking mobile targets using sensor networks, in IEEE International Workshop on Sensor Network Protocols and Applications, Anchorage, 2003, pp. 73–77

    Google Scholar 

  16. Z. Wang, W. Lou, Z. Wang, J. Ma, H. Chen, A novel mobility management scheme for target tracking in cluster-based sensor networks, in International Conference on Distributed Computing in Sensor Systems (Springer, Santa Barbara, 2010), pp. 172–186

    Google Scholar 

  17. J. Reich F. Zhao, J. Shin, Information-driven dynamic sensor collaboration. IEEE Signal Process. Mag. 19, 61–72 (2002)

    Article  Google Scholar 

  18. Y. Zhang J. Qian, X. Jin, Energy-efficient node selection for acoustic source localization in wireless sensor network, in 6th International Conference on Wireless Communications Networking and Mobile Computing, Shenzhen, 2010, pp. 1–5

    Google Scholar 

  19. L.M. Kaplan, Global node selection for localization in a distributed sensor network. IEEE Trans. Aerosp. Electron. Syst. 42(1), 113–135 (2006)

    Article  Google Scholar 

  20. A. Capponi, C. Pilotto, G. Golino, A. Farina, L. Kaplan, Algorithms for the selection of the active sensors in distributed tracking: comparison between frisbee and GNS methods, in 9th International Conference on Information Fusion, Florence, 2006, pp. 1–8

    Google Scholar 

  21. D. Estrin C. Intanagonwiwat, R. Govinda, Directed diffusion: a scalable and robust communication paradigm for sensor networks, in 6th Annual International Conference on Mobile Computing and Networking, San Diego, 2003, pp. 56–67

    Google Scholar 

  22. T. Yum X. Zhu, L. Shen, Hausdorff clustering and minimum energy routing for wireless sensor networks. IEEE Trans. Veh. Technol. 58(2), 990–997 (2009)

    Article  Google Scholar 

  23. U. Madhow, J. Singh, R. Kumar et al., Multiple-target tracking with binary proximity sensors. ACM Trans. Sensor Netw. 8(1), 3–13 (2011)

    Google Scholar 

  24. D. Wang, Y. Wang, Energy-efficient node selection for target tracking in wireless sensor networks. Int. J. Distrib. Sensor Netw. 9(1), 1–6 (2013)

    Article  Google Scholar 

  25. C.S. Raghavendra, K.M. Sivalingam, T. Znati, Wireless Sensor Networks (Springer, New York, 2006)

    MATH  Google Scholar 

  26. S. Blackman, R. Popoli, Design and Analysis of Modern Tracking Systems (book). (Artech House, Norwood, 1999)

    Google Scholar 

  27. R. Stolkin, I. Florescu, Probability of detection and optimal sensor placement for threshold based detection systems, IEEE Sensors J. 9(1), 57–60 (2009)

    Article  Google Scholar 

  28. P.C. Mahalanobis, On the generalised distance in statistics., in Proceedings of the National Institute of Sciences of India, vol. 2 (Baptist Mission Press, 1936), pp. 49–55

    Google Scholar 

  29. L. Guibas F. Zhao, Wireless Sensor Networks: An Information Processing Approach (Morgan Kaufmann Publishers, Amsterdam, 2004)

    Google Scholar 

  30. S.M.H. Jalilolghadr, M. Sabaei. Proposed a new algorithm for real-time applications in routing of wireless sensor networks, in Proceedings of the International Conference on Management and Artificial Intelligence, Bali, 2011, pp. 1–3

    Google Scholar 

  31. B. Krishnamachari, D. Estrin, S. Wicker, Modelling data-centric routing in wireless sensor networks. IEEE Infocom 2, 39–44 (2002)

    Google Scholar 

  32. T. Cormen, C. Leiserson, R. Rivest, C. Stein, Introduction to Algorithms (The MIT Press, Cambridge, 2010)

    MATH  Google Scholar 

  33. M. Morari F. Borrelli, A. Bemporad, Predictive Control for Linear and Hybrid Systems (Cambridge University Press, Cambridge, 2015)

    MATH  Google Scholar 

  34. M. Johansson, A. Rantzer, Computation of piecewise quadratic lyapunov functions for hybrid systems, in 1997 European Control Conference (ECC), Brussels, July 1997, pp. 2005–2010

    Google Scholar 

  35. N. Sandell, R. Oltafi-Saber, Distributed tracking in sensor networks with limited sensing range, in American Control Conference, Seattle, 2008, pp. 3158–3163

    Google Scholar 

  36. V.J. Aidala, Kalman filter behavior in bearings-only tracking applications. IEEE Trans. Aerosp. Electron. Syst. 15(1), 29–39 (1979)

    Article  Google Scholar 

  37. S. Nardone, A.G. Lindgren, K. Gong, Fundamental properties and performance of conventional bearings-only target motion analysis. IEEE Trans. Autom. Control 29(9), 775–787 (1984)

    Article  Google Scholar 

  38. P. Rong, M. Sichitiu, Angle of arrival localization for wireless sensor networks, in 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, SECON’06, vol. 1 (IEEE, Reston, 2006), pp. 374–382

    Google Scholar 

  39. P. Kułakowski, J. Vales-Alonso, E. Egea-López, W. Ludwin, J. García-Haro, Angle-of-arrival localization based on antenna arrays for wireless sensor networks. Comput. Electr. Eng. 36(6), 1181–1186 (2010)

    Article  Google Scholar 

  40. B. Ristic, S. Arulampalam, J. McCarthy, Target motion analysis using range-only measurements: algorithms, performance and application to ingara isar data. Technical report, Defence Science and Technology Organisation Electronics and Surveillance Research, Salisbury, 2001

    Google Scholar 

  41. N Peach, Bearings-only tracking using a set of range-parameterised extended kalman filters. IEEE Proc. Control Theory Appl. 142(1), 73–80 (1995)

    Article  Google Scholar 

  42. F. Zhao, L. Guibas (eds.), Information Processing in Sensor Networks (Springer, Berlin/Heidelberg, 2003) pp. 412–415

    Google Scholar 

  43. D.P. Spanos, R. Olfati-Saber, R.M. Murray, Approximate distributed Kalman filtering in sensor networks with quantifiable performance, in Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, Los Angeles, 2005

    Google Scholar 

  44. J. Thomas T. Cover, Elements of Information Theory (Wiley, Hoboken, 2006)

    MATH  Google Scholar 

  45. P. Tichavsky, C.H. Muravchik, A. Nehorai, Posterior cramer-rao bounds for discrete-time nonlinear filtering. IEEE Trans. Signal Process. 46(5), 1386–1396 (1998)

    Article  Google Scholar 

  46. N. Gordon, B. Ristic, S. Arulampalam, Beyond the Kalman Filter (Artech House, Boston, 2004)

    MATH  Google Scholar 

  47. C. Yang, L. M. Kaplan, E. Blasch, M. Bakich, Optimal placement of heterogeneous sensors for targets with Gaussian priors. IEEE Trans. Aerosp. Electron. Syst. 49(3), 1637–1653 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Air Force Office of Scientific Research Grant No. AFOSR FA9550-15-1-0330.

Author information

Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, USA

    Alexander A. Soderlund & Mrinal Kumar

Authors
  1. Alexander A. Soderlund
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mrinal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander A. Soderlund .

Editor information

Editors and Affiliations

  1. Air Force Office of Scientific Research, Air Force Research Laboratory, Arlington, VA, USA

    Erik Blasch

  2. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  3. Information Directorate, Air Force Research Laboratory, Rome, NY, USA

    Alex Aved

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Soderlund, A.A., Kumar, M. (2018). Optimization of Multi-target Tracking Within a Sensor Network Via Information Guided Clustering. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95504-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95503-2

  • Online ISBN: 978-3-319-95504-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation