Skip to main content
SpringerLink
Log in

A novel SMC-PHD filter based on particle compensation

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, the particle impoverishment problem introduced by the resampling step, together with the high computational burden problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this work, a novel SMC-PHD filter based on particle compensation is proposed to solve above problems. Firstly, according to a comprehensive analysis on the particle impoverishment problem, a new particle generating mechanism is developed to compensate the particles. Then, all the particles are integrated into the SMC-PHD filter framework. Simulation results demonstrate that, in comparison with the SMC-PHD filter, proposed PC-SMC-PHD filter is capable of overcoming the particle impoverishment problem, as well as improving the processing rate for a certain tracking accuracy in different scenarios.

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

Similar content being viewed by others

References

  1. BAR-SHALOM Y, FORTMANN T E. Tracking and data Association [M]. San Diego, CA, USA: Academic, 1988: 15–18.

    MATH  Google Scholar 

  2. FORTMANN T E, BAR-SHALOM Y, SCHEFFE, M. Sonar tracking of multiple targets using joint probabilistic data association [J]. IEEE Journal of Oceanic Engineering, 1983, 8(3): 173–184.

    Article  Google Scholar 

  3. BLACKMAN S. Multiple hypothesis tracking for multiple target tracking [J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 19(1): 5–18.

    Article  Google Scholar 

  4. MAHLER R. Statistical multisource-multitarget information fusion [M]. Norwood: Artech House, 2007: 20–25.

    MATH  Google Scholar 

  5. MAHLER R. Multi-target Bayes filtering via first-order multi-target moments [J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152–1178.

    Article  Google Scholar 

  6. MAHLER R. PHD filters of higher order in target number [J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523–1543.

    Article  Google Scholar 

  7. VO B N, MA W K. The Gaussian mixture probability hypothesis density filter [J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091–4104.

    Article  Google Scholar 

  8. VO B N, SINGH S, DOUCET A. Sequential Monte Carlo methods for Bayesian multi-target filtering with random finite sets [J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224–1245.

    Article  Google Scholar 

  9. SHI Zhi-guo, LIU Yong-kang, HONG Shao-hua, CHEN Ji-ming, SHEN Xue-min. POSE: Design of hardware-friendly particle-based observation selection PHD filter [J]. IEEE Transactions on Industrial Electronics, 2014, 61(4): 1944–1956.

    Article  Google Scholar 

  10. LIAN Feng, HAN Chong-zhao, LIU Wei-feng. Novel extension of the probability hypothesis density filter for tracking multi-target in clutter with an unknown intensity [J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46: 2066–2078.

    Article  Google Scholar 

  11. WU Xin-hui, HUANG Gao-ming, GAO Jun. Particle filters for probability hypothesis density filter with the presence of unknown measurement noise covariance [J]. Chinese Journal of Aeronautics, 2013, 26(6): 1517–1523.

    Article  Google Scholar 

  12. LI Tian, CORCHADO J M, SIYAU M F. A particle dyeing approach for track continuity for the SMC-PHD filter [C]// 17th International Conference on Information Fusion. Salamanca, Spain, 2014: 1–8.

    Google Scholar 

  13. LI Wen-ling, JIA Ying-min. Gaussian mixture PHD filter for jump Markov models based on best-fitting Gaussian approximation [J]. Signal Processing, 2011, 91: 1036–1042.

    Article  MATH  Google Scholar 

  14. ZHENG Yun-mei, SHI Zhi-guo, LU Rong-xing. An efficient data-driven particle PHD filter for multi-target tracking [J]. IEEE Transactions on Industrial Informatics, 2012, 9(4): 2318–2326.

    Article  Google Scholar 

  15. SWAIN A, CLARK D. The single-group PHD filter: An analytic solution [C]// 17th International Conference on Information Fusion. Salamanca, Spain, 2014, 41–48.

    Google Scholar 

  16. LI Tian-cheng, SUN Shu-dong, Sattar T P. High-speed Sigma-gating SMC-PHD filter [J]. Signal Processing, 2013, 93(3): 2586–2593.

    Article  Google Scholar 

  17. WANG Ya-dong, WU Jian-kang, KASSIM A. Data-driven probability hypothesis density filter for visual tracking [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(8): 1085–1095.

    Article  Google Scholar 

  18. CARVA H, MORAL P D. Optimal Nonlinear Filtering in GPS/INS Integration [J]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(2): 835–849.

    Article  Google Scholar 

  19. ARULA M S, MASKELL S, GORDON N, CLAPP T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188.

    Article  Google Scholar 

  20. FEARNH P. Markov chain Monte Carlo, sufficient statistics and particle filters [J]. Journal of Computional and Graphical Statistics, 2002, 11(4): 848–862.

    Article  MathSciNet  Google Scholar 

  21. YE L, WANG J L, ZHANG Q. Genetic resampling particle Filter [J]. Acta Automatica Sinica, 2007, 33(8): 885–897.

    Google Scholar 

  22. BOLIC M, DJURIC P M, HONG S J. Resampling algorithms and architectures for distributed particle filters [J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2442–2450.

    Article  MathSciNet  MATH  Google Scholar 

  23. SCHU D, VO B T, VO B N. A consistent metric for performance evaluation of multi-object filters [J]. IEEE Transactions on Signal Processing, 2008 56(8): 3447–3457.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai, 264001, China

    Cong-an Xu  (徐从安), You He  (何友), Fu-cheng Yang  (杨富程), Tao Jian  (简涛) & Hai-peng Wang  (王海鹏)

  2. School of Electronics and Computer Science University of Southampton, Southampton, SO17 1BJ, UK

    Fu-cheng Yang  (杨富程)

  3. 302 Unit, Second Artillery Engineering University, Xi’an, 710025, China

    Tian-mei Li  (李天梅)

Authors
  1. Cong-an Xu  (徐从安)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. You He  (何友)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fu-cheng Yang  (杨富程)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Jian  (简涛)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hai-peng Wang  (王海鹏)
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tian-mei Li  (李天梅)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to You He  (何友).

Additional information

Foundation item: Projects(61671462, 61471383, 61671463, 61304103) supported by the National Natural Science Foundation of China; Project(ZR2012FQ004) supported by the Natural Science Foundation of Shandong Province, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, Ca., He, Y., Yang, Fc. et al. A novel SMC-PHD filter based on particle compensation. J. Cent. South Univ. 24, 1826–1836 (2017). https://doi.org/10.1007/s11771-017-3591-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-017-3591-9

Key words

Search

Navigation