Development of Tracking Filter for the Location Awareness of Moving Objects in Ubiquitous Computing

  • Lee, Yang-Weon (Department of Information and Communication Engineering, Honam University)
  • Published : 2008.03.31

Abstract

In this paper, I have presented a new approach which can track moving objects in unknown environments. This scheme is important in providing a computationally feasible alternative to complete enumeration of JPDA which is intractable. I have proved that given an artificial measurement and track's configuration, proposed scheme converges to a proper plot in a finite number of iterations. In this light, even if the performance is enhanced by using the relaxation, we also note that the difficulty in tuning the parameters of the relaxation scheme is critical aspect of this suggestion.

Keywords

References

  1. Alspach D. L.: A Gaussian sum approach to multi-target identification tracking problem, Automatica, Vol. 11 (1975) 285-296 https://doi.org/10.1016/0005-1098(75)90044-8
  2. Reid D. B.: An algorithm for tracking multiple targets, IEEE Trans. on Automat. Contr., Vol. 24 (1979) 843-854 https://doi.org/10.1109/TAC.1979.1102177
  3. Bar-Shalom Y.: Extension of probabilistic data associatiation filter in multitarget tracking, in Proc. 5th Symp. Nonlinear Estimation Theory and its Application (1974) 16-21
  4. Sengupta, D., and Iltis, R. A.:Neural solution to the multitarget tracking data association problem, IEEE Trans. on AES, AES-25 (1989) 96-108
  5. Kuczewski R.: Neural network approaches to multitarget tracking, In proceedings of the IEEE ICNN conference (1987)
  6. Fortmann T. E., Bar-Shalom Y., and Scheffe M.: Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association, IEEE J. Oceanic Engineering, Vol. OE-8 (1983) 173-184
  7. Fitzgerald R.J.: Development of practical PDA logic for multitarget tracking by microprocessor, In Proceedings of the American Controls Conference, Seattle, Wash. (1986) 889-898
  8. Fortmann T. E., Bar-Shalom Y.: Tracking and Data Association, Orland Acdemic Press (1988)
  9. Lee Y. W., and Jeong H.: A Neural Network Approach to the Optimal Data Association in Multi-Target Tracking, Proc. of WCNN'95, INNS Press
  10. Luenberger D. G.: Linear and Nonlinear Programming, Addition-wesley Publishing Co. (1984)
  11. Hiriart-Urruty J. B. and Lemarrecchal C.: Convex Analysis and Minimization Algorithms I, Springer-Verlag (1993)
  12. Cichocki A., and Unbenhauen R.:Neural networks for optimization and signal processing, Wiley, New York (1993)
  13. Emile Aarts and Jan Korst: Simulated annealing and Boltzmann Machines, Wily, New York (1989)
  14. Singer, R.A.: Estimating optimal tracking filter performance for manned maneuvering targets, IEEE Transactions on Aerospace and Electronic Systems, Vol. 6 (1970) 473-483 https://doi.org/10.1109/TAES.1970.310128
  15. Kalman R.E.: A new approach to linear filtering and prediction problems, Trans. ASME, (J. Basic Eng.,) Vol.82 (1960)
  16. Platt J.C., and Barr A.H.: Constrained Differential Optimization, Proceedings of the 1987 IEEE NIPS Conf., Denver (1987)