Advertisement

Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking

  • Zhuoran Zhang
  • Changqiang Huang
  • Dali Ding
  • Shangqin Tang
  • Bo Han
  • Hanqiao HuangEmail author
Original Paper

Abstract

As a commonly used filtering method for nonlinear non-Gaussian systems, particle filters (PFs) have been successfully applied in the field of maneuvering target tracking. However, particle impoverishment is a major obstacle to the PF performance. To overcome this defect, this paper combines the hummingbirds optimization algorithm (HOA) with a standard PF and proposes an HOA-based PF (HOA-PF) for maneuvering target tracking. The proposed filter treats the particles as individual hummingbirds, simulates the honey-collecting process of hummingbirds in nature and moves the particles as a whole to the high-likelihood region by performing self-searching and guided-searching phases. Moreover, to enhance the particle diversity, the mutation method of the following birds in the HOA is improved. Thus, the distribution of particles in the HOA-PF is reasonable. The results of experiments on the univariate nonstationary growth model and the maneuvering target tracking problem demonstrate the effectiveness of the proposed method.

Keywords

Particle filter Particle impoverishment Hummingbirds optimization algorithm Maneuvering target tracking 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61601505.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest to disclose.

References

  1. 1.
    Schmidt, S.F.: The Kalman filter—its recognition and development for aerospace applications. J. Guid. Control Dyn. 4, 4–7 (1981)Google Scholar
  2. 2.
    Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)CrossRefGoogle Scholar
  3. 3.
    Arasaratnam, I., Haykin, S.: Cubature Kalman filters. IIEEE Trans. Autom. Contr. 54(6), 1254–1269 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Proces. 50(2), 174–188 (2002)CrossRefGoogle Scholar
  5. 5.
    Gonzales, C., Dubuisson, S.: Combinatorial resampling particle filter: an effective and efficient method for articulated object tracking. Int. J. Comput. Vis. 112(3), 255–284 (2015)CrossRefzbMATHGoogle Scholar
  6. 6.
    Wang, P., Gao, R.X.: Adaptive resampling-based particle filtering for tool life prediction. J. Manuf. Syst. 37, 528–534 (2015)CrossRefGoogle Scholar
  7. 7.
    Lamberti, R., Petetin, Y., Desbouvries, F., Septier, F.: Independent resampling sequential Monte Carlo algorithms. IEEE Trans. Signal Proces. 65(20), 5318–5333 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Fan, Y.R., Huang, G.H., Baetz, B.W., Huang, K.: Development of a copula-based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence. Water Resour. Res. 53(6), 4850–4875 (2017)CrossRefGoogle Scholar
  9. 9.
    Zhou, N., Meng, D., Lu, S.: Estimation of the dynamic states of synchronous machines using an extended particle filter. IEEE Trans. Power Syst. 28(4), 4152–4161 (2013)CrossRefGoogle Scholar
  10. 10.
    Du, X., Wang, Y., Hu, H., Liu, H.: The attitude inversion method of geostationary satellites based on unscented particle filter. Adv. Space Res. 61(8), 1984–1996 (2018)CrossRefGoogle Scholar
  11. 11.
    Xia, B., Sun, Z., Zhang, R., Lao, Z.: A cubature particle filter algorithm to estimate the state of the charge of lithium-ion batteries based on a second-order equivalent circuit model. Energies 10(4), 457 (2017)CrossRefGoogle Scholar
  12. 12.
    Jing, L., Vadakkepat, P.: Interacting MCMC particle filter for tracking maneuvering target. Digit Signal Process. 20(2), 561–574 (2010)CrossRefGoogle Scholar
  13. 13.
    Han, H., Ding, Y., Hao, K.: A new immune particle filter algorithm for tracking a moving target. In: Proceedings of International Conference on Natural Computation, vol. 6. IEEE, pp. 3248–3252 (2010)Google Scholar
  14. 14.
    Bi, J., Zhang, T., Yu, H., Yan, J.: State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter. Appl. Energy 182, 558–568 (2016)CrossRefGoogle Scholar
  15. 15.
    Tian, Y., Lu, C., Wang, Z., Tao, L.: Artificial fish swarm algorithm-based particle filter for li-ion battery life prediction. Math. Probl. Eng. 3, 1–10 (2014)Google Scholar
  16. 16.
    Zhao, J., Li, Z.: Particle filter based on particle swarm optimization resampling for vision tracking. Expert Syst. Appl. 37(12), 8910–8914 (2010)CrossRefGoogle Scholar
  17. 17.
    Walia, G.S., Kapoor, R.: Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search. Expert Syst. Appl. 41(14), 6315–6326 (2014)CrossRefGoogle Scholar
  18. 18.
    Gao, M.L., Li, L.L., Sun, X.M., Yin, L.J., Li, H.T., Luo, D.S.: Firefly algorithm (FA) based particle filter method for visual tracking. Optik 126(18), 1705–1711 (2015)CrossRefGoogle Scholar
  19. 19.
    Rohilla, R., Sikri, V., Kapoor, R.: Spider monkey optimisation assisted particle filter for robust object tracking. IET Comput. Vis. 11(3), 207–219 (2017)CrossRefGoogle Scholar
  20. 20.
    Zhang, Z., Huang, C., Huang, H., et al.: An optimization method: hummingbirds optimization algorithm. J. Syst. Eng. Electron. 29(2), 386–404 (2018)CrossRefGoogle Scholar
  21. 21.
    Fu, C.M., Jiang, C., Chen, G.S., Liu, Q.M.: An adaptive differential evolution algorithm with an aging leader and challengers mechanism. Appl. Soft. Comput. 57, 60–73 (2017)CrossRefGoogle Scholar
  22. 22.
    Wang, E., Jia, C., Tong, G., Qu, P.P., Lan, X.Y., Pang, T.: Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm. Adv. Space Res. 61(5), 1260–1272 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Zhuoran Zhang
    • 1
  • Changqiang Huang
    • 1
  • Dali Ding
    • 1
  • Shangqin Tang
    • 1
  • Bo Han
    • 1
  • Hanqiao Huang
    • 2
    Email author
  1. 1.Aeronautics Engineering CollegeAir Force Engineering UniversityXi’anChina
  2. 2.Unmanned System Research InstituteNorthwestern Polytechnical UniversityXi’anChina

Personalised recommendations