Architectures for Particle Filtering

Chapter

Abstract

There are many applications in which particle filters outperform traditional signal processing algorithms. Some of these applications include tracking, joint detection and estimation in wireless communication, and computer vision. However, particle filters are not used in practice for these applications mainly because they cannot satisfy real-time requirements. This chapter discusses several important issues in designing an efficient resampling architecture for high throughput parallel particle filtering. The resampling algorithm is developed in order to compensate for possible error caused by finite precision quantization in the resampling step. Communication between the processing elements after resampling is identified as an implementation bottleneck, and therefore, concurrent buffering is incorporated in order to speed up communication of particles among processing elements. The mechanism utilizes a particle-tagging scheme during quantization to compensate possible loss of replicated particles due to the finite precision effect. Particle tagging divides replicated particles into two groups for systematic redistribution of particles to eliminate particle localization in parallel processing. The mechanism utilizes an efficient interconnect topology for guaranteeing complete redistribution of particles even in case of potential weight unbalance among processing elements. The architecture supports high throughput and ensures that the overall parallel particle filtering execution time scales with the number of processing elements employed.

Keywords

Assure 

References

  1. 1.
    A. Doucet, N. de Freitas, and N. Gordon, Eds., Sequential Monte Carlo Methods in Practice, New York: Springer Verlag, 2001.MATHGoogle Scholar
  2. 2.
    E. R. Beadle and P. M. Djurić, “A fast weighted Bayesian bootstrap filter for nonlinear model state estimation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 33, pp. 338–343, 1997.CrossRefGoogle Scholar
  3. 3.
    D. Crisan, P. Del Moral, and T. J. Lyons, “Non-linear filtering using branching and interacting particle systems,” Markov processes and Related Fields, vol. 5, no. 3, pp. 293–319, 1999.MathSciNetMATHGoogle Scholar
  4. 4.
    J. S. Liu, R. Chen, and W. H. Wong, “Rejection control and sequential importance sampling,” Journal of American Statistical Association, vol 93, no. 443, pp. 1022–1031, 1998.MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    M. S. Arulampalam, S. Maskell, N. Gordon, T. Clapp, “A Tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Journal of Signal Processing, 2002.Google Scholar
  6. 6.
    J. Carpenter, P. Clifford, and P. Fearnhead, “An improved particle filter for non-linear problems,” IEE Proceedings-F: Radara, Sonar and Navigation, vol. 146, pp. 2–7,1999.Google Scholar
  7. 7.
    N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “A novel approach to nonlinear and non-Gaussian Bayesian state estimation,” IEE Proceedings-F: Radar, Sonar and Navigation, vol. 140, pp. 107–113, 1993.Google Scholar
  8. 8.
    P. M. Djurić, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo, and J. Miguez, “Particle filtering,” IEEE Signal Processing Magazine, vol. 20, no. 5, pp. 19–38, September 2003.CrossRefGoogle Scholar
  9. 9.
    C. Berzuini, N. G. Best, W. R. Gilks, and C. Larizza, “Dynamic conditional independence models and Markov chain Monte Carlo methods,” Journal of the American Statistical Association, vol. 92, pp. 1403–1412, 1997.MathSciNetCrossRefGoogle Scholar
  10. 10.
    A. Kong, J. S. Liu, and W. H. Wong, “Sequential imputations and Beyesian missing data problems,” Journal of Americal Statistical Association, vol. 89, no. 425, pp. 278–288, 1994.MATHCrossRefGoogle Scholar
  11. 11.
    J. S. Liu and R. Chen, “Blind convolution via sequential imputations,” Journal of American Statistical Association, vol. 90, no. 430, pp. 567–576, 1995.MATHCrossRefGoogle Scholar
  12. 12.
    D. Crisan, “Particle filters - A theoretical perspective,” Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G. de Freitas, and N. J. Gordon, Eds. New York: Springer-Verlag, 2001.Google Scholar
  13. 13.
    G. Kitagawa, “Monte Carlo lteration and smoother for non-Gaussian nonlinear state space models,” Journal of Computational and Graphical Statistics, 5:1–25, 1996.MathSciNetGoogle Scholar
  14. 14.
    E. Baker, “Reducing Bias and Inefficiency in The Selection Algorithms,” Proceedings of Second International Conference on Genetic Algorithms, pp. 14–2, 1987.Google Scholar
  15. 15.
    J. S. Liu and R. Chen, “Sequential Monte Carlo methods for dynamic systems,” Journal of the American Statistical Association, vol. 93, pp. 1032–1044, 1998.MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    S. Hong, S.-S. Shin, P. M. Djurić, and M. Bolić, “An efficient fixed-point implementation of residual systematic resampling scheme for high-speed particle filters,” VLSI Signal Processing, vol. 44, no. 1, pp. 47–62, 2006.MATHCrossRefGoogle Scholar
  17. 17.
    S. Hong and P. M. Djurić, “High-throughput scalable parallel resampling mechanism for effective redistribution of particles,” IEEE Transactions on Signal Processing, vol. 54, no. 3, pp. 1144–1155, 2006.CrossRefGoogle Scholar
  18. 18.
    M. Bolić, P. M. Djurić, and S. Hong, “New resampling algorithms for particle filters”, IEEE ICASSP, 2003.Google Scholar
  19. 19.
    M. Bolić, P. M. Djurić, and S. Hong, “Resampling algorithms for particle filters suitable for parallel VLSI implementation,” IEEE CISS, 2003.Google Scholar
  20. 20.
    M. Bolić, P. M. Djurić, and S. Hong, “Resampling algorithms for particle filters: A computational complexity perspective,” EURASIP Journal of Applied Signal Processing, no. 15, pp. 2267–2277, November 2004.Google Scholar
  21. 21.
    R. Tessier and W. Burleson, “Reconfigurable computing and digital signal processing: A survey,” Journal of VLSI Signal Processing, May/June 2001.Google Scholar
  22. 22.
    Xilinx, “Virtex-II Platform FPGA Handbook,” 2000.Google Scholar
  23. 23.
    N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “A novel approach to nonlinear and non-Gaussian Bayesian state estimation,” IEE Proceedings F, vol. 140, pp. 107–113, 1993.Google Scholar
  24. 24.
    S. Hong, M. Bolić, and P. M. Djurić, “An efficient fixed-point implementation of residual systematic resampling scheme for high-speed particle filters,” IEEE Signal Processing Letters, vol. 11, no. 5, May 2004.Google Scholar
  25. 25.
    M. Bolić, P. M. Djurić, and S. Hong, “Resampling algorithms and architectures for distributed particle filters,” IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2442–2450, 2005.MathSciNetCrossRefGoogle Scholar
  26. 26.
    S.-S. Chin and S. Hong, “VLSI design and implementation of high-throughput processing elements for parallel particle filters,” IEEE SCS, 2003.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringStony Brook UniversityStony BrookUSA
  2. 2.College of Information & Communication, Division of Computer and Communications EngineeringKorea UniversitySeoulKorea

Personalised recommendations