International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Suboptimal Algorithm of the Optimal Bayesian Filter Based on the Receding Horizon Strategy

  • Kim, Yong-Shik (School of Mechanical Engineering, Pusan National University) ;
  • Hong, Keum-Shik (School of Mechanical Engineering, Pusan National University)
  • Published : 2003.06.01
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Abstract

The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase in memory size and computation quantity over time. In this paper, the inevitable predicament of the optimal Bayesian filter is resolved in a suboptimal fashion through the use of a receding horizon strategy. As a result, the problems of memory and computational requirements are diminished. As a priori information, the horizon initial state is estimated from the validated measurements on the receding horizon. Consequently, the suboptimal algorithm proposed allows for real time implementation.

Keywords

References

  1. Tracking and Data Association Y. B. Shalom;T. E. Fortmann
  2. Proc. of the IEEE Conference on Decision and Control Adaptive nonlinear filtering for tracking with measurements of uncertain origin Y. Bar-Shalom;A. G. Jaffer
  3. Applied Optimal Estimation A. Gelb
  4. Matrix Computations(3rd Edition) G. H. Golub;C. F. Van Loan
  5. Proc. of the 1999 IEEE International Conference on Control Applications Receding horizon FIR filter with estimated horizon initial state and its application to aircraft engine systems S. H. Han;P. S. Kim;W. H. Kwon
  6. Stochastic Processes and Filtering Theory A. H. Jazwinski
  7. Fundamentals of Statistical Signal Processing: Estimation Theory S. M. Kay
  8. IEEE Trans. on Automatic Control v.26 no.3 Optimal tracking of a maneuvering target in clutter R. J. Kenefic https://doi.org/10.1109/TAC.1981.1102720
  9. IEEE Trans. on Automatic Control v.44 no.9 A receding horizon Kalman FIR filter for discrete time-invariant systems W. H. Kwon;P. S. Kim;P. G. Park https://doi.org/10.1109/9.788554
  10. IEEE Trans. on Automatic Control v.44 no.9 Receding horizon recursive state estimation K. V. Ling;K. W. Lim https://doi.org/10.1109/9.788546
  11. Automatica v.36 no.10 Estimation and detection of unknown inputs using optimal FIR filter S. H. Park;P. S. Kim;O. K. Kwon;W. H. Kwon https://doi.org/10.1016/S0005-1098(00)00063-7
  12. IEEE Trans. on Automatic Control v.18 no.12 New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments R. A. Singer;R. G. Sea https://doi.org/10.1109/TAC.1973.1100421
  13. IEEE Trans. on Information Theory v.20 no.4 Derivation and evaluation of improved tracking filters for use in dense multitarget environments R. A. Singer;R. G. Sea;K. B. Housewright https://doi.org/10.1109/TIT.1974.1055256
  14. IEEE Trans. on Military Electronics v.8 no.4 An optimal data association problem in surveillance theory R. W. Sittler https://doi.org/10.1109/TME.1964.4323129