Joint Multiple Target Tracking and Classification Using Controlled Based Cheap JPDA-Multiple Model Particle Filter in Cluttered Environment

  • Zahir Messaoudi
  • Abdelaziz Ouldali
  • Mourad Oussalah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

In this paper, we address the problem of jointly tracking and classifying several targets in cluttered environment. It is assumed that the motion model of a given target belongs to one of several classes. We propose to use the multiple model particle filter (MMPF) to perform nonlinear filtering with switching dynamic models. Moreover, the principle of joint probabilistic data association (JPDA) is used to determine the measurements origin. Besides, the joint probabilities are calculated using Fitzgerald’s had hoc formulation (Cheap JPDA) whose efficiency has been proven in the literature. On the other hand, a controller based on the quality of the innovation has been implemented in order to tune the number of particles. The feasibility and the performances of the proposal have been demonstrated using a set of Monte Carlo simulations dealing with two maneuvering targets.

Keywords

Cluttered Environment Initial State Vector Maneuvering Target Constant Velocity Model Augmented State Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Blackman, S.S., Popoli, R.: Design and analysis of modern tracking systems. Artech House (1999)Google Scholar
  2. 2.
    Bar-Shalom, Y., Fortmann, T.: Tracking and Data Association. Mathematics in Science and Engineering, vol. 179. Academic Press, London (1988)MATHGoogle Scholar
  3. 3.
    Bar-Shalom, Y., Xiao-Rong, Li.: Estimation and Tracking: Principles, Techniques, and Software. Artech Houes (1993)Google Scholar
  4. 4.
    Anderson, B.D.O., Moore, J.B.: Optimal Filtering. Prentice Hall, Englewood Cliffs (1979)MATHGoogle Scholar
  5. 5.
    Doucet, A., De Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)MATHGoogle Scholar
  6. 6.
    Gustafsson, F.: Adaptive Filtering and Change Detection. John Wiley and Sons Ltd, Chichester (2000)CrossRefGoogle Scholar
  7. 7.
    Ripley, B.D.: Stochastic Simulation. John Wiley, Chichester (1988)Google Scholar
  8. 8.
    Fitzgerald, R.J.: Development of practical PDA logic for multitarget tracking by microprocessor. In: Bar-Shalom, Y., Noorwood, M.A. (eds.) Multitarget-Multisensor tracking: Advanced Application, pp. 1–23. Artech House (1990)Google Scholar
  9. 9.
    Messaoudi, Z., Oussalah, M., Ouldali, A.: Data association for maneuvering targets using controlled based JPDAF particle filter. In: Proc of SMC, Irlande and United Kingdom (2007)Google Scholar
  10. 10.
    Branko, R., Sanjeev, A., Neil, G.: Beyond the Kalman filter. Particle filter for tracking application. Artech Housse, Boston (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zahir Messaoudi
    • 1
  • Abdelaziz Ouldali
    • 1
  • Mourad Oussalah
    • 2
  1. 1.Military Polytechnic SchoolAlgiersAlgeria
  2. 2.Electronics, Electrical and Computer Engineering EdgbastonUniversity of BirminghamBirminghamUK

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