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An Improved PHD Filter Based on Dynamic Programming

  • Meng FangEmail author
  • Wenguang Wang
  • Dong Cao
  • Yan Zuo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)

Abstract

Traditional PHD filter for detecting and tracking weak targets does not work well in the case of low detection probability. In this paper, an improvement of PHD filtering based on dynamic programming is proposed. The method takes advantage of the correlation among the multi-frame data. The result of dynamic programming is applied to PHD filter for getting stable detecting and tracking effect. Monte Carlo simulation results show that the improved method is superior to the PHD filter under low detection probability.

Keywords

Weak targets PHD filter Dynamic programming 

Notes

Acknowledgments

The work is supported by NSFC (No. 61771028 and No. 61673146).

References

  1. 1.
    Yang, W., Fu, Y., Pan, X.G., Zhang, Z., Li, X.: Track-before-detect technique for dim targets: an overview. Acta Electronic Sinica 42(09), 1786–1793 (2014)Google Scholar
  2. 2.
    Mahler, R.: A theoretical foundation for the Stein-Winter probability hypothesis density (PHD) multi-target tracking approach. In: Proceedings of the MSS National Symposium on Sensor and Data Fusion, San Antonio, TX, pp. 99–117 (2000)Google Scholar
  3. 3.
    Vo, B.-N., Ma, W.-K.: The Gaussian mixture probability hypothesis density filters. IEEE Trans. Sig. Process. 54(11), 4091–4104 (2006)CrossRefGoogle Scholar
  4. 4.
    Vo, B.-N., Singh, S., Doucet, A.: Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Trans. Aerosp. Electron. Syst. 41(4), 1224–1245 (2005)CrossRefGoogle Scholar
  5. 5.
    Han, C., Zhu, H., Duan, Z.: Multi Sensor Information Fusion. Tsinghua University Press, Beijing (2010)Google Scholar
  6. 6.
    Hu, Z.: A Study of Multi-target Tracking Based on Random Finite Set Using Radar. Xidian University (2015)Google Scholar
  7. 7.
    Zhang, H.: Probability Hypothesis Density Filter Algorithm and its Application in Multi-Targets Tracking. Harbin Engineering University (2012)Google Scholar
  8. 8.
    Wan, Y., Wang, S., Weihua, W.: Dynamic programming track before detect for maneuvering dim targets. J. Sig. Process. 29(05), 548–590 (2013)Google Scholar
  9. 9.
    Tian, S., Wang, G., He, Y.: Multi-target tracking with probability hypothesis density particle filter. J. Nav. Aeronaut. Eng. Inst. (04), 417–420 + 430 (2007)Google Scholar
  10. 10.
    Jing, P., Shiyou, X., Li, X., Chen, Z.: Performance evaluation of multiple target tracking: a survey. Syst. Eng. Electron. 36(1), 2127–2132 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of EIEBeihang UniversityBeijingChina

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