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DART: Distributed Particle Filter Algorithm with Resampling Tree for Ultimate Real-Time Capability

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Abstract

A novel Distributed Particle Filter Algorithm with Resampling Tree, called DART, is proposed in this paper, where particles are resampled by Branch Resampling and Root Resampling in a flexible tree-like structure. Though sampling and weight calculation can be executed in parallel on a group of Processing Elements, resampling is the bottleneck for distributed particle filters since it requires the knowledge of the whole particle set. Conventional approaches to accelerate resampling on distributed platforms often introduce extra procedure other than the standard processing flow and achieve acceleration limited by linear speedup. By introducing the proposed algorithm, where Branch Resampling can be executed in parallel with sampling and weight calculation, the number of particles in the final sequential implemented Root Resampling can be reduced in an exponential relationship with the depth of the tree. With the same linear speedup in sampling and weight calculation steps, the overall speedup achieved in DART surpasses linear boundary and outperforms state-of-art approaches. The corresponding implementation architecture, which possesses unique features of hardware efficiency and scalability, is also presented. The prototype of the algorithm with 8 PEs is implemented on a Xilinx Virtex-IV Pro FPGA (XC4VFX100-12FF1152) under BOT system. With 8192 particles, the input observation can achieve 63.3 kHz at a clock speed of 80 MHz.

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References

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

    Article  Google Scholar 

  2. Sankaranarayanan, A. C., Srivastava, A., & Chellappa, R. (2008). Algorithmic and architectural optimizations for computationally efficient particle filtering. IEEE Transactions on Image Processing, 17(5), 737–748.

    Article  MathSciNet  Google Scholar 

  3. Gentner C., Munoz E., Khider M., Staudinger E., Sand S., & Dammann A. (2012). Particle filter based positioning with 3GPP-LTE in indoor environments. Position Location and Navigation Symposium (PLANS), IEEE/ION, 301–308.

  4. Hongjun, Z., & 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 

  5. Kai, W., Yun-Hui, L., & Luyang, L. (2014). A simple and parallel algorithm for real-time robot localization by fusing monocular vision and odometry/AHRS sensors. IEEE/ASME Transactions on Mechatronics, 19(4), 1447–1457.

    Article  Google Scholar 

  6. Sz-Pin H., Jun-Wei Q., Chi-Chung L., & Yu-Chee T. (2014). Wearable localization by particle filter with the assistance of inertial and visual sensors. 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 52–57.

  7. Arshad, I., Syed, W. S., & Shamin, K. (2014). Non-linear moving target tracking: a particle filter approach. International Journal of Computer and Communication System Engineering, 1(1), 20–26.

    Google Scholar 

  8. Tian, Q., Salcic, Z., Wang, K. I., & Pan, Y. (2015). A hybrid indoor localization and navigation system with map matching for pedestrians using smartphones. Sensors, 2015, 30759–30783.

    Article  Google Scholar 

  9. Putta R., Misra M., & Kapoor D. (2015). Smartphone based Indoor tracking using magnetic and indoor maps. Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 I.E. Tenth International Conference on, pp. 1–6.

  10. Qian J., Ma J., Ying R., Liu P., & Pei L. (2013). An improved indoor localization method using smartphone inertial sensors. Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on, pp. 1–7.

  11. Bao, H., & Wong, W. (2014). A novel map-based dead-reckoning algorithm for indoor localization. Sensors, 3, 44–63.

    Google Scholar 

  12. Valentin R., & Mahesh K. M. (2013). HiMLoc: indoor smartphone localization via activity aware pedestrian dead reckoning with selective crowdsourced WiFi fingerprinting. International Conference on Indoor Positioning and Indoor Navigation.

  13. Yuan Y., Yubin Z., & Kyas M. (2014). GeoF: a geometric Bayesian filter for indoor position tracking in mixed LOS/NLOS conditions. 11th Workshop on Positioning, Navigation and Communication (WPNC), 1–6, 12–13.

  14. 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  MATH  Google Scholar 

  15. Zhang Y., Sathyan T., Hedley M., Leong P. H. W., & Pasha A. (2012). Hardware efficient parallel particle filter for tracking in wireless networks. 2012 I.E. 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), pp. 1734–1739.

  16. Chitchian, M., Simonetto, A., Amesfoort, A. S., & Keviczky, T. (2013). Distributed computation particle filters on GPU architectures for real-time control applications. IEEE Transactions on Control Systems Technology, 21(6), 2224–2238.

    Article  Google Scholar 

  17. Pan, Y., Zheng, N., Tian, Q., Yan, X., & Huan, R. (2013). Hierarchical resampling algorithm and architecture for distributed particle filters. Journal of Signal Processing Systems, 71(3), 237–246.

    Article  Google Scholar 

  18. Carpenter, J., Clifford, P., & Fearnhead, P. (1999). Improved particle filter for nonlinear problems. IEE Proceedings of Radar, Sonar and Navigation, 146(1), 2–7.

    Article  Google Scholar 

  19. Kerem P., & Oguz T. (2011). Parallelization of particle filter based localization and map matching algorithm on multicore/manycore architectures. IEEE Intelligent Vehicles Symposium, pp. 820–826.

  20. Xu Y., Liu J., Ma L., & Peng L. (2010). WLAN indoor tracking method via improved particle filter algorithm. Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on, pp. 1078–1082.

  21. Bolic M., Hong S., & Djuric P. M. (2002). Finite precision effect on performance and complexity of particle filters for bearing-only tracking. Conference Record of the 36th Asilomar Conference on Signals, Systems and Computers, vol. 1. pp. 838–842.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (61204030, 61302129), Zhejiang Provincial Natural Science Foundation of China (LY15F020008), Zhejiang Provincial Nonprofit Technology Research Projects (2014C31045) and China Scholarship Council.

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Authors and Affiliations

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

    Qinglin Tian

  2. College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China

    Yun Pan

  3. Department of Electrical and Computer Engineering, University of Auckland, Auckland, New Zealand

    Zoran Salcic

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

    Ruohong Huan

Authors
  1. Qinglin Tian
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  2. Yun Pan
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  3. Zoran Salcic
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  4. Ruohong Huan
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Corresponding author

Correspondence to Yun Pan.

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Tian, Q., Pan, Y., Salcic, Z. et al. DART: Distributed Particle Filter Algorithm with Resampling Tree for Ultimate Real-Time Capability. J Sign Process Syst 88, 29–42 (2017). https://doi.org/10.1007/s11265-016-1110-0

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  • DOI: https://doi.org/10.1007/s11265-016-1110-0

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