Skip to main content

Particle Filtering Algorithms for Tracking a Maneuvering Target Using a Network of Wireless Dynamic Sensors


We investigate the problem of tracking a maneuvering target using a wireless sensor network. We assume that the sensors are binary (they transmit '1' for target detection and '0' for target absence) and capable of motion, in order to enable the tracking of targets that move over large regions. The sensor velocity is governed by the tracker, but subject to random perturbations that make the actual sensor locations uncertain. The binary local decisions are transmitted over the network to a fusion center that recursively integrates them in order to sequentially produce estimates of the target position, its velocity, and the sensor locations. We investigate the application of particle filtering techniques (namely, sequential importance sampling, auxiliary particle filtering and cost-reference particle filtering) in order to efficiently perform data fusion, and propose new sampling schemes tailored to the problem under study. The validity of the resulting algorithms is illustrated by means of computer simulations.


  1. Luo RC, Yih C-C, Su KL: Multisensor fusion and integration: approaches, applications and future research directions. IEEE Sensors Journal 2002, 2(2):107–119. 10.1109/JSEN.2002.1000251

    Article  Google Scholar 

  2. Chong C-Y, Kumar SP: Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE 2003, 91(8):1247–1256. 10.1109/JPROC.2003.814918

    Article  Google Scholar 

  3. Zhao F, Guibas L: Wireless Sensor Networks. Morgan Kaufman, New York, NY, USA; 2004.

    Google Scholar 

  4. Brooks RR, Ramanathan P, Sayeed AM: Distributed target classification and tracking in sensor networks. Proceedings of the IEEE 2003, 91(8):1163–1171. 10.1109/JPROC.2003.814923

    Article  Google Scholar 

  5. Doherty L, Warneke BA, Boser BE, Pister KSJ: Energy and performance considerations for smart dust. International Journal of Parallel and Distributed Systems and Networks 2001, 4(3):121–133.

    Google Scholar 

  6. Patwari N, Hero AO III, Perkins M, Correal NS, O'Dea RJ: Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing 2003, 51(8):2137–2148. 10.1109/TSP.2003.814469

    Article  Google Scholar 

  7. Savvides A, Girod L, Srivastava MB, Estrin D: Localization in sensor networks. In Wireless Sensor Networks. Edited by: Raghavendra CS, Sivalingham KM, Znati T. Kluwer Academic, Boston, Mass, USA; 2004.

    Google Scholar 

  8. Savvides A, Han C-C, Srivastava MB: Dynamic fine-grained localization in ad-hoc networks of sensors. Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MOBICOM '01), July 2001, Rome, Italy 166–179.

    Chapter  Google Scholar 

  9. Ihler AT, Fisher JW III, Moses RL, Willsky AS: Nonparametric belief propagation for self-calibration in sensor networks. Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN '04), April 2004, Berkeley, Calif, USA 225–233.

    Google Scholar 

  10. Artés-Rodríguez A: Decentralized detection in sensor networks using range information. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), May 2004, Montreal, Quebec, Canada 2: 265–268.

    Google Scholar 

  11. Míguez J, Bugallo MF, Djurić PM: Decision fusion for distributed target tracking using cost reference particle filtering. Proceedings of 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, Turkey

    Google Scholar 

  12. Djurić PM, Vemula M, Bugallo MF, Míguez J: Non-cooperative localization of binary sensors. Proceedings of 13th IEEE Workshop on Statistical Signal Processing (SSP '05), July 2005, Bordeaux, France

    Google Scholar 

  13. Liu JS, Chen R: Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 1998, 93(443):1032–1044. 10.2307/2669847

    Article  MathSciNet  Google Scholar 

  14. Doucet A, Godsill S, Andrieu C: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 2000, 10(3):197–208. 10.1023/A:1008935410038

    Article  Google Scholar 

  15. Arulumpalam MS, Maskell S, Gordon N, Klapp T: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 2002, 50(2):174–188. 10.1109/78.978374

    Article  Google Scholar 

  16. Djurić PM, Kotecha JH, Zhang J, et al.: Particle filtering. IEEE Signal Processing Magazine 2003, 20(5):19–38. 10.1109/MSP.2003.1236770

    Article  Google Scholar 

  17. Crisan D, Doucet A: A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing 2002, 50(3):736–746. 10.1109/78.984773

    Article  MathSciNet  Google Scholar 

  18. Doucet A, de Freitas N, Gordon N: An introduction to sequential Monte Carlo methods. In Sequential Monte Carlo Methods in Practice. Edited by: Doucet A, de Freitas N, Gordon N. Springer, New York, NY, USA; 2001:4–14. chapter 1

    Chapter  Google Scholar 

  19. Douc R, Cappé O, Moulines E: Comparison of resampling schemes for particle filtering. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis (ISPA '05), September 2005, Zagreb, Croatia 64–69.

    Google Scholar 

  20. Pitt MK, Shephard N: Auxiliary variable based particle filters. In Sequential Monte Carlo Methods in Practice. Edited by: Doucet A, de Freitas N, Gordon N. Springer, New York, NY, USA; 2001:273–293. chapter 13

    Chapter  Google Scholar 

  21. Míguez J, Bugallo MF, Djurić PM: A new class of particle filters for random dynamic systems with unknown statistics. EURASIP Journal on Applied Signal Processing 2004, 2004(15):2278–2294. 10.1155/S1110865704406039

    MathSciNet  MATH  Google Scholar 

  22. Gustafsson F, Gunnarsson F, Bergman N, et al.: Particle filters for positioning, navigation, and tracking. IEEE Transactions on Signal Processing 2002, 50(2):425–437. 10.1109/78.978396

    Article  Google Scholar 

  23. Rappaport TS: Wireless Communications: Principles and Practice. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2001.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Joaquín Míguez.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Míguez, J., Artés-Rodríguez, A. Particle Filtering Algorithms for Tracking a Maneuvering Target Using a Network of Wireless Dynamic Sensors. EURASIP J. Adv. Signal Process. 2006, 083042 (2006).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: