A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets

  • Donka Angelova
  • Lyudmila Mihaylova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


This paper considers the joint state and parameter estimation of extended targets. Both the target kinematic states, position and speed, are estimated with the target extent parameters. The developed algorithm is applied to a ship, whose shape is modelled by an ellipse. A Bayesian sampling algorithm with finite mixtures is proposed for the evaluation of the extent parameters whereas a suboptimal Bayesian interacting multiple model (IMM) filter estimates the kinematic parameters of the maneuvering ship. The algorithm performance is evaluated by Monte Carlo comparison with a particle filtering approach.


Markov Chain Monte Carlo Monte Carlo Monte Carlo Algorithm Data Augmentation Markovian Jump System 


  1. 1.
    Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, New York (2001)MATHGoogle Scholar
  2. 2.
    Liu, J.: Monte Carlo Strategies in Scientific Computing. Springer, New York (2003)Google Scholar
  3. 3.
    Storvik, G.: Particle filters in state space models with the presence of unknown static parameters. IEEE. Trans. of Signal Processing 50(2), 281–289 (2002)CrossRefGoogle Scholar
  4. 4.
    Doucet, A., Tadic, V.: Parameter estimation in general state-space models using particle methods. Ann. Inst. Stat. Math. 55(2), 409–422 (2003)MATHMathSciNetGoogle Scholar
  5. 5.
    Salmond, D., Parr, M.: Track maintenance using measurements of target extent. IEE Proc.-Radar Sonar Navig. 150(6), 389–395 (2003)CrossRefGoogle Scholar
  6. 6.
    Ristic, B., Salmond, D.: A study of a nonlinear filtering problem for tracking an extended target. In: Proc. Seventh Intl. Conf. on Information Fusion, pp. 503–509 (2004)Google Scholar
  7. 7.
    Vermaak, J., Ikoma, N., Godsill, S.: Sequential Monte Carlo Framework for Extended Object Tracking. IEE Proc.-Radar Sonar Navig. 152(5), 353–363 (2005)CrossRefGoogle Scholar
  8. 8.
    Jilkov, V., Li, X., Rong, A.D.: Estimation of Markovian Jump Systems with Unknown Transition Probabilities through Bayesian Sampling. In: Dimov, I.T., Lirkov, I., Margenov, S., Zlatev, Z. (eds.) NMA 2002. LNCS, vol. 2542, pp. 307–315. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Diebolt, J., Robert, C.P.: Estimation of Finite Mixture Distributions through Bayesian Sampling. J. of Royal Statist. Soc. B 56(4), 363–375 (1994)MATHMathSciNetGoogle Scholar
  10. 10.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and Software. Wiley, New York (2001)CrossRefGoogle Scholar
  11. 11.
    Stephens, M.: Bayesian methods for mixture of normal distributions. PhD Thesis (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Donka Angelova
    • 1
  • Lyudmila Mihaylova
    • 2
  1. 1.Institute for Parallel ProcessingBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of Communication SystemsLancaster UniversityLancasterUK

Personalised recommendations