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.






Similar content being viewed by others
References
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Míguez, J. (2007). Analysis of parallelizable resampling algorithms for particle filtering. Signal Processing, 87, 3155–3174.
Acknowledgments
The authors would like to thank Zhuoran Zhao and Ye Wang for their contributions in the hardware implementation.
Author information
Authors and Affiliations
Corresponding author
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
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-012-0712-4