Skip to main content
SpringerLink
Log in

Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

In this paper, we introduce a hierarchical resampling (HR) algorithm and architecture for distributed particle filters (PFs). While maintaining the same accuracy as centralized resampling in statistics, the proposed HR algorithm decomposes the resampling step into two hierarchies including intermediate resampling (IR) and unitary resampling (UR), which suits PFs for distributed hardware implementation. Also presented includes a residual cumulative resampling (RCR) method that pipelines and accelerates the UR step. The corresponding architecture, when compared with traditional distributed architectures, eliminates the particle redistribution step, and has such advantages as short execution time and high memory efficiency. The prototype containing 8 PEs has been developed in Xilinx Virtex IV FPGA (XC4VFX100-12FF1152) for the bearings-only tracking (BOT) problem, and the result shows that the input observations can be processed at 37.21 KHz with 8 K particles and a clock speed of 80 MHz.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

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

    Google Scholar 

  2. Doucet, A., & Wang, X.-D. (2005). Monte Carlo methods for signal processing: a review in the statistical signal processing context. IEEE Signal Processing Magazine, 22(6), 152–170.

    Article  Google Scholar 

  3. Djuric, P. M., Kotecba, J. H., Zhang, J., Huang, Y., Gbirmai, T., Bugallo, M. F., et al. (2003). Particle filtering. IEEE Signal Processing Magazine, 20(5), 19–38.

    Article  Google Scholar 

  4. Saha, S., Bambha, N. K., & Bhattacharyya, S. S. (2010). Design and implementation of embedded computer vision system based on particle filters. Computer Vision and Image Understanding, 114(11), 1203–1214.

    Article  Google Scholar 

  5. Gustafsson, F., Gunnarsson, F., Fergman, N., Rofssell, U., Jamsson, J., Karlsson, R., et al. (2002). Particle Filters for Positioning, Navigation and Tracking. IEEE Transactions on Signal Processing, 50(2), 425–437.

    Article  Google Scholar 

  6. Zhou, H., & Sakane, S. (2008). Sensor planning for mobile robot localization—a hierarchical approach using a Bayesian network and a particle filter. IEEE Transactions on Robotics, 24(2), 481–487.

    Article  Google Scholar 

  7. Athalye, A., Bolic, M., Hong, S., & Djuric, P. M. (2005). Generic hardware architectures for sampling and resampling in particle filters. EURASIP Journal on Applied Signal Processing, 17, 2888–2902.

    Google Scholar 

  8. Hong, S., Bolic, M., & Djuric, P. M. (2004). An efficient fixed-point implementation of residual resampling scheme for high-speed particle filters. IEEE Signal Processing Letters, 11(5), 482–485.

    Article  Google Scholar 

  9. Hong, S.-H., Shi, Z.-G., Chen, J.-M., & Chen, K.-S. (2010). A low-power memory-efficient resampling architecture for particle filters. Circuits, Systems, and Signal Processing, 29(1), 155–167.

    Article  MATH  Google Scholar 

  10. Zheng, N., Pan, Y., Yan, X., & Huan, R. (2011). Local weight mean comparison scheme and architecture for high-speed particle filters. Electronics Letters, 47(2), 142–144.

    Article  Google Scholar 

  11. Hong, S., Shi, Z., & Chen, K. (2010). Easy-hardware-implementation MMPF for maneuvering target tracking: algorithm and architecture. Journal of Signal Processing Systems, 61, 259–269.

    Article  Google Scholar 

  12. Bolic, M., Djuric, P. M., & Hong, S. (2005). Resampling algorithms and architectures for distributed particle filters. IEEE Transactions on Signal Processing, 53(7), 2442–2450.

    Article  MathSciNet  Google Scholar 

  13. Hong, S., Bolic, M., & Djuric, P. M. (2006). High-throughput scalable parallel resampling mechanism for effective redistribution of particles. IEEE Transactions on Signal Processing, 54(3), 1144–1155.

    Article  Google Scholar 

  14. Sankaranarayanan, A. C., Srivastava, A., & Chellappa, R. (2008). Algorithmic and architectural optimizations for computationally efficient particle filtering. IEEE Transactions on Image Processing, 17(5), 737–747.

    Article  MathSciNet  Google Scholar 

  15. Miao, L., Zhang, J. J., Chakrabarti, C., Papandreou-Suppappola, A. (2010). A new parallel implementation for particle filters and its application to adaptive waveform design. IEEE Workshop on Signal Processing Systems, 19–24.

  16. Zheng N., Pan Y., Yan X., and Huan R., (2012). Hierarchical resampling architecture for distributed particle filters. IEEE International Conference on Acoustics, Speech and Signal Processing.

  17. Míguez, J. (2007). Analysis of parallelizable resampling algorithms for particle filtering. Signal Processing, 87, 3155–3174.

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Zhuoran Zhao and Ye Wang for their contributions in the hardware implementation.

Author information

Authors and Affiliations

  1. Department of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China

    Yun Pan & Ning Zheng

  2. College of Electrical Engineering, Zhejiang University, Hangzhou, China

    Qinglin Tian & Xiaolang Yan

  3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Ruohong Huan

Authors
  1. Yun Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ning Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qinglin Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolang Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruohong Huan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Pan.

Additional information

This research was supported by Zhejiang Provincial Natural Science Foundation of China (No. LQ12F04002) and the National Natural Science Foundation of China (No. 61204030). Part of the work in this paper was presented at the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pan, Y., Zheng, N., Tian, Q. et al. Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters. J Sign Process Syst 71, 237–246 (2013). https://doi.org/10.1007/s11265-012-0712-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-012-0712-4

Keywords

Search

Navigation