Skip to main content
SpringerLink
Log in

Target tracking by fusion of random measures

  • Original paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper we propose fusion methods for tracking a single target in a sensor network. The sensors use sequential Monte Carlo (SMC) techniques to process the received measurements and obtain random measures of the unknown states. We apply standard particle filtering (SPF) and cost-reference particle filtering (CRPF) methods. For both types of filtering, the random measures contain particles drawn from the state space. Associated to the particles, the SPF has weights representing probability masses, while the CRPF has user-defined costs measuring the quality of the particles. Summaries of the random measures are sent to the fusion center which combines them into a global summary. Similarly, the fusion center may send a global summary to the individual sensors that use it for improved tracking. Through extensive simulations and comparisons with other methods, we study the performance of the proposed algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Varshney P.K. (1997). Multisensor data fusion. Electron. Commun. Eng. J. 9(6): 245–253

    Article  Google Scholar 

  2. Luo R.C., Yih C.C. and Su K.L. (2002). Mutlisensor fusion integration: approaches applications, and future research direction. IEEE Sensors J. 2(2): 107–119

    Article  Google Scholar 

  3. Anderson D.O. and Moore J.B. (1979). Optimal Filtering. Prentice-Hall, Inc. Englewood Cliffs

    MATH  Google Scholar 

  4. Bar-Shalom Y. and Fortmann T.E. (1988). Tracking and Data Association. Academic, New York

    MATH  Google Scholar 

  5. Chang, K.C., Chong, C.Y., Bar-Shalom, Y.: Distributed Estimation in Distributed Sensor Networks in Stochastic Large Scale Systems. M. Dekker (1992)

  6. Chang K.C., Saha R.K. and Bar-Shalom Y. (1997). On optimal track-to-track fusion. IEEE Trans. Aeros. Electron. Systems 33(4): 1271–1276

    Article  Google Scholar 

  7. Doucet A., de Freitas N. and Gordon N. (2001). Sequential Monte Carlo Methods in Practice. Springer, Heidelberg, New York

    MATH  Google Scholar 

  8. Míguez J., Bugallo M.F. and Djurić P.M. (2004). A new class of particle filters for random dynamical systems with unknown statistics. EURASIP J. Appl. Sig. Process. 15: 2278–2294

    Article  Google Scholar 

  9. Sheng, X., Hu, Y.-H., Ramanathan, P.: Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network. In: Fourth International Symposium on Information Processing in Sensor Networks, (IPSN 2005) vol. 15, April 2005, 181–188

  10. Coates, M.: Distributed particle filters for sensor networks. In: Proceedings of the third International Symposium on Information Processing in Sensor Networks (IPSN-04), ACM Press, New York: April 26–27, 2004, pp. 99–107

  11. Rosencrantz, M., Gordon, G.J., Thrun, S.: Decentralized sensor fusion with distributed particle filters. In: UAI ’03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence, August 7–10, Acapulco, Mexico 493–500 (2003)

  12. Challa, S., Palaniswami, M., Shilton, A.: Distributed data fusion using support vector machines. In: Proceedings of the Fifth International Conference on Information FUSION 2002, vol. 2, pp. 881–885 (2002)

  13. Rey W.J.J. (1983). Introduction to Robust and Quasi–Robust Statistical Methods. Springer, Heidelberg, New York

    MATH  Google Scholar 

  14. Kotecha J.H. and Djurić P.M. (2003). Gaussian particle filtering. IEEE Trans. Sig. Process. 51(10): 2592–2601

    Article  Google Scholar 

  15. McLachlan G.J. and Krishnan T. (1997). The EM Algorithm and Extensions. Wiley, New York

    MATH  Google Scholar 

  16. Gordon N.J., Salmond D.J. and Smith A.F.M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F 140(2): 107–113

    Google Scholar 

  17. Gustafsson F., Gunnarsson F., Bergman N., Forssell U., Jansson J., Karlsson R. and Nordlund P. (2002). Particle filters for positioning, navigation and tracking. IEEE Trans. Sig. Process. 50(2): 425–436

    Article  Google Scholar 

  18. Gilardoni G.L. (2005). Accuracy of posterior approximations via χ2 and harmonic divergences. J. Statist. Plann. Inf. 128(1): 475–487

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794-2350, USA

    Mahesh Vemula, Mónica F. Bugallo & Petar M. Djurić

Authors
  1. Mahesh Vemula
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mónica F. Bugallo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Petar M. Djurić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahesh Vemula.

Additional information

This work has been supported by the National Science Foundation under Award CCF-0515246 and the Office of Naval Research under Award N00014-06-1-0012.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vemula, M., Bugallo, M.F. & Djurić, P.M. Target tracking by fusion of random measures. SIViP 1, 149–161 (2007). https://doi.org/10.1007/s11760-007-0012-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-007-0012-9

Keywords

Search

Navigation