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Signal, Image and Video Processing

, Volume 1, Issue 2, pp 133–148 | Cite as

Low computation and low latency algorithms for distributed sensor network initialization

  • M. BorkarEmail author
  • V. Cevher
  • J. H. McClellan
Original paper

Abstract

In this paper, we show how an underlying system’s state vector distribution can be determined in a distributed heterogeneous sensor network with reduced subspace observability at the individual nodes. The presented algorithm can generate the initial state vector distribution for networks with a variety of sensor types as long as the collective set of measurements from all the sensors provides full state observability. Hence the network, as a whole, can be capable of observing the target state vector even if the individual nodes are not capable of observing it locally. Initialization is accomplished through a novel distributed implementation of the particle filter that involves serial particle proposal and weighting strategies that can be accomplished without sharing raw data between individual nodes. If multiple events of interest occur, their individual states can be initialized simultaneously without requiring explicit data association across nodes. The resulting distributions can be used to initialize a variety of distributed joint tracking algorithms. We present two variants of our initialization algorithm: a low complexity implementation and a low latency implementation. To demonstrate the effectiveness of our algorithms we provide simulation results for initializing the states of multiple maneuvering targets in smart sensor networks consisting of acoustic and radar sensors.

Keywords

Monte Carlo methods Initialization Distributed processing Sensor networks Heterogeneous sensors Data fusion 

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References

  1. 1.
    Manyika, J., Durrant-Whyte, H. Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Prentice Hall (1994)Google Scholar
  2. 2.
    Aslam, J., Butler, Z., Constantin, F., Crespi, V., Cybenko, G., Rus, D.: Tracking a moving object with a binary sensor network. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems. Los Angeles, 5-7 November 2003, pp. 150–161 (2003)Google Scholar
  3. 3.
    Snelick R., Uludag U., Mink A., Indovina M. and Jain A. (2005). Large scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans. Pattern Anal. Mach. Intell. 27(3): 450–455 CrossRefGoogle Scholar
  4. 4.
    Dogandzic A. and Zhang B. (2006). Distributed estimation and detection for sensor networks using hidden markov random field models. IEEE Trans. Signal Process. 54(8): 3200–3215 Google Scholar
  5. 5.
    Yedidia J., Freeman W.T. and Weiss Y. (2001). Generalized belief propagation. Adv. Neural Inf. Process. Systems 13: 689–695 Google Scholar
  6. 6.
    Chellappa R., Jain A., Eds. (1993). Markov Random Fields. Theory and Application. Academic, Boston Google Scholar
  7. 7.
    Sudderth, E.B., Ihler, A.T., Freeman, W.T., Willsky, A.S.: Nonparametric belief propagation. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, 18–20 June 2003, vol. 1, pp. 605–612 (2003)Google Scholar
  8. 8.
    Isard, M.: PAMPAS: real-valued graphical models for computer vision. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, 18-20 June 2003, vol. 1, pp. 613-620 (2003)Google Scholar
  9. 9.
    Ihler, A.T., Sudderth, E.B., Freeman, W.T., Willsky, A.S.: Efficient multiscale sampling from products of Gaussian mixtures. In: Neural Information Processing Systems Vol. 17. Vancouver, 9-11 December 2003 (2003)Google Scholar
  10. 10.
    Ihler A.T., Fisher J.W., Moses R.L. and Willsky A.S. (2005). Nonparametric belief propagation for self-localization of sensor networks. IEEE J. Selected Areas Commun. 23(4): 809–819 CrossRefGoogle Scholar
  11. 11.
    Coates, M.J.: Distributed particle filtering for sensor networks. In: Third International Symposium on Information Processing in Sensor Networks. Berkeley, 26-27 April 2004, pp. 99–107 (2004)Google Scholar
  12. 12.
    Leichter, I., Lindenbaum, M., Rivlin, E.: A probabilistic framework for combining tracking algorithms. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, June 27–July 2 2004, vol. 2, pp. 445–451 (2004)Google Scholar
  13. 13.
    Borkar, M., Cevher, V., McClellan, J.H.: Estimating target state distributions in a distributed sensor network using a Monte-Carlo approach. In: 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, 28–30 Sept. 2005, pp. 305–310 (2005)Google Scholar
  14. 14.
    Borkar, M., Cevher, V., McClellan, J.H.: A Monte-Carlo method for initializing distributed tracking algorithms with acoustic propagation delay compensation. To appear in J. VLSI Signal Process. Syst. Invited paper, also available at http://www.umiacs.umd.edu/users/volkan/JVLSIMilind.pdfGoogle Scholar
  15. 15.
    Cevher, V., Chellappa, R., Shah, F., Velmurugan, R., McClellan, J.H.: An acoustic multi-target tracking system using random sampling consensus. To appear in Proceedings of the 2007 IEEE Aerospace Conference. Big Sky, 3–10 March 2007 (2007)Google Scholar
  16. 16.
    Cevher, V., Velmurugan, R., McClellan, J.H.: A range-only multiple target particle filter tracker. In 2006 IEEE International Conference on Acoustics, Speech and Signal Processing. Toulouse, May 2006, vol. 4, pp. 905–908 (2006)Google Scholar
  17. 17.
    Brookner E. (1988). Tracking and Kalman Filtering Made Easy. Wiley, New York Google Scholar
  18. 18.
    Kalata, P.R., Murphy, K.M.: α−β target tracking with track rate variations. In: IEEE Proceedings of the Twenty-Ninth Southeastern Symposium on Systems Theory. Cookeville, 9–11 March 1997, pp. 70–74 (1997)Google Scholar
  19. 19.
    Doucet, A.: On sequential simulation-based methods for Bayesian filtering. Tech. Rep. CUED/F-INFENG/TR.310, Department of Engineering, University of Cambridge (2001)Google Scholar
  20. 20.
    Hammersley, J.M., Handscomb, D.C.: Monte Carlo Methods. Methuen (1964)Google Scholar
  21. 21.
    Munkres, J.R.: Analysis on Manifolds. Perseus Books (1990)Google Scholar
  22. 22.
    Bar-Shalom Y. and Fortmann T. (1988). Tracking and Data Association. Academic, New york zbMATHGoogle Scholar
  23. 23.
    Isard M. and Blake A. (1998). Condensation—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1): 5–28 CrossRefGoogle Scholar
  24. 24.
    Parzen E. (1992). On estimation of a probability density function and mode. Ann. Mathe. Statist. 33(3): 1065–1076 MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.Center for Signal and Image Processing, School of ECEGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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