Advertisement

MOPSO Optimized Radar CBMeMBer Forward-Backward Smoothing Filter

  • Jiazheng Pei
  • Yong Huang
  • Yunlong Dong
  • Xiaolong Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

For the tracking of multiple maneuvering targets under radar observations, the Cardinality-Balanced Multi-Bernoulli based Sequential Monte-Carlo Filter (SMC-CBMeMBer) tracking algorithm gets its shortcomings that the estimation of number is inaccurate and the state estimation accuracy is degraded. This paper presents an improved tracking algorithm based on SMC-CBMeMBer smoothing filter. In the prediction process, the algorithm uses Multi-objectIve Particle Swarm Optimization (MOPSO), combined with the measured values at the current moment, to move the particles to the location where the posterior probability density distribution takes a larger value; Besides the smooth recursive method is used to smooth the filter value with multi-target measurement data, and the estimation accuracy of the algorithm is improved on the basis of sacrificing certain operation efficiency. The simulation results show that compared with the traditional filter and smoothing methods, the proposed algorithm performs better in terms of the accuracy of the estimation of the number of maneuvering targets and the accuracy of the target state estimation.

Keywords

Particle swarm optimization Particle filter CBMeMBer filter Forward-backward smoothing 

References

  1. 1.
    Mahler, R.: Statistical Multisource-Multitarget Information Fusion, pp. 5–11. Artech House, Boston (2007)zbMATHGoogle Scholar
  2. 2.
    Mahler, R.: Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1152–1178 (2003)CrossRefGoogle Scholar
  3. 3.
    Mahler, R.: PHD filters of higher order in target number. IEEE Trans. Aerosp. Electron. Syst. 43(4), 1523–1543 (2007)CrossRefGoogle Scholar
  4. 4.
    Vo, B.T., See, C.M., Ma, N., et al.: Multi-sensor joint detection and tracking with the Bernoulli filter. IEEE Trans. Aerosp. Electron. Syst. 48(2), 1385–1402 (2012)CrossRefGoogle Scholar
  5. 5.
    Vo, B.T., Vo, B.N., Cantoni, A.: The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Trans. Sig. Process. 57(2), 409–423 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Vo, B.T., Clark, D., Vo, B.N., et al.: Bernoulli forward-backward smoothing for joint target detection and tracking. IEEE Trans. Sig. Process. 59(9), 4473–4477 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wong, S., Vo, B.T., Papi, F.: Bernoilli forward-backward smoothing for track-before-detect. IEEE Sig. Process. Lett. 21(6), 727–731 (2014)CrossRefGoogle Scholar
  8. 8.
    Liu, J.S., Chen, R., Logvinenko, T.: A theoretical framework for sequential importance sampling with resampling. In: Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice, pp. 225–246. Springer, New York (2001).  https://doi.org/10.1007/978-1-4757-3437-9_11CrossRefGoogle Scholar
  9. 9.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1941–1948. IEEE Service Center, Piscataway (1995)Google Scholar
  10. 10.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-45356-3_83CrossRefGoogle Scholar
  11. 11.
    Ouyang, C., Ji, H., Li, C.: Improved multi-target multi-Bernoulli filter. IET Radar Sonar Navig. 6(6), 458–464 (2012)CrossRefGoogle Scholar
  12. 12.
    Zhou, G., Pelletier, M., Kirubarajan, T., et al.: Statically fused converted position and doppler measurement Kalman filters. IEEE Trans. Aerosp. Electron. Syst. 50(1), 300–318 (2014)CrossRefGoogle Scholar
  13. 13.
    Yoon, J.H., Kim, D.Y., Bae, S.H., et al.: Joint initialization and tracking of multiple moving objects using doppler information. IEEE Trans. Sig. Process. 59(7), 3447–3452 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Vo, B.N., Ma, W.K.: The Gaussian mixture probability hypothesis density filter. IEEE Trans. Sig. Process. 54(11), 4091–4104 (2006)CrossRefGoogle Scholar
  15. 15.
    Vo, B.T., Vo, B.N., Cantoni, A.: Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Trans. Sig. Process. 55(7), 3553–3567 (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ristic, B., Vo, B.N., Clark, D., et al.: A metric for performance evaluation of multi-target tracking algorithms. IEEE Trans. Sig. Process. 59(7), 3452–3457 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Jiazheng Pei
    • 1
  • Yong Huang
    • 1
  • Yunlong Dong
    • 1
  • Xiaolong Chen
    • 1
  1. 1.Naval Aviation UniversityYantaiChina

Personalised recommendations