Federated Information Mode-Matched Filters in ACC Environment

  • Kim Yong-Shik (Graduate School of Systems and Information Engineering, University of Tsukuba) ;
  • Hong Keum-Shik (School of Mechanical Engineering, Pusan National University)
  • Published : 2005.06.01


In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.



  1. Y. Bar-Shalom, X. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley & Sons, INC, New York, 2001
  2. N. A. Carson, 'Federated square root filter for decentralized parallel processes,' IEEE Transactions on Aerospace and Electronic Systems, vol. 26, no. 3, pp. 517-525, 1990 https://doi.org/10.1109/7.106130
  3. N. A. Carlson and M. P. Berarducci, 'Federated Kalman filter simulation results,' Journal of the Institute of Navigation, vol. 41, no. 3, pp. 297-321, 1994
  4. D. S. Caveney, Multiple Target Tracking in the Adaptive Cruise Control Environment Using Multiple Models and Probabilistic Data Association, M. S. Thesis, University of California, Berkeley, U. S. A., 1999
  5. K. C. Chang, T. Zhi, and R. K. Saha, 'Performance evaluation of track fusion with information matrix filter,' IEEE Trans. on Aerospace and Electronic Systems, vol. 38, no. 2, pp. 455-466, 2002 https://doi.org/10.1109/TAES.2002.1008979
  6. C. Y. Chong, S. Mori, and K. C. Chang, 'Distributed multitarget multisensor tracking,' in Bar-Shalom, Y. (Ed.), Multitarget-Multisensor Tracking: Advanced Applications, Artech House, Norwood, MA, 1990
  7. F. Dufour and M. Mariton, 'Passive sensor data fusion and maneuvering target tracking,' in: Bar-Shalom, Y. (Ed.), Multitarget-Multisensor Tracking: Applications and Advances, Artech House, Norwood, MA, Chapter 3, pp. 65-92, 1992
  8. J. P. Helferty, 'Improved tracking of maneuvering targets: The use of turn-rate distributions for acceleration modeling,' IEEE Trans. on Aerospace and Electronic Systems, vol. 32, no. 4, pp. 1355-1361, 1996 https://doi.org/10.1109/7.543856
  9. V. P. Jilkov, D. S. Angelova, and TZ. A. Semerdjiev, 'Design and comparison of modeset adaptive IMM algorithms for maneuvering target tracking,' IEEE Trans. on Aerospace and Electronic Systems, vol. 35, no. 1, pp. 343-350, 1999 https://doi.org/10.1109/7.745704
  10. Y. S. Kim and K. S. Hong, 'An IMM algorithm for tracking maneuvering vehicles in an adaptive cruise control environment,' International Journal of Control, Automation, and Systems, vol. 2, no. 3, pp. 310-318, September 2004
  11. T. G. Lee, 'Centralized Kalman filter with adaptive measurement fusion: its application to a GPS/SDINS integration system with an additional sensor,' International Journal of Control, Automation, and Systems, vol. 1, no. 4, pp. 444-452, December 2003
  12. B. J. Lee, Y. H. Joo, and J. B. Park, 'An Intelligent tracking method for a maneuvering target,' International Journal of Control, Automation, and Systems, vol. 1, no. 1, pp. 93-100, March 2003
  13. S. J. Lee, J. H. Hong, and K. S. Yi, 'A modeling and control of intelligent cruise control systems,' Trans. of the KSME, A, vol. 25, no. 2, pp. 283-288, 2001
  14. X. Li and Y. Bar-Shalom, 'Design of an interacting multiple model algorithm for air traffic control tracking,' IEEE Trans. on Control Systems Technology, vol. 1, no. 3, pp. 186-194, 1993 https://doi.org/10.1109/87.251886
  15. I. K. Moon and K. S. Yi, 'Vehicle tests of a longitudinal control law for application to stopand- go cruise control,' KSME International Journal, vol. 16, no. 9, pp. 1166-1174, 2002
  16. A. G. O. Mutambara, Decentralized Estimation and Control for Multisensor Systems, CRC Press, Boca Raton, 1998
  17. E. Semerdjiev and L. Mihaylova, 'Variable- and fixed-structure augmented interacting multiplemodel algorithms for maneuvering ship tracking based on new ship models,' International Journal of Applied Mathematics and Computer Science, vol. 10, no. 3, pp. 591-604, 2000
  18. Y. Zhu, Z. You, J. Zhao, K. Zhang, and X. Li, 'The optimality for the distributed Kalman filtering fusion,' Automatica, vol. 37, no. 9, pp. 1489-1493, 2001 https://doi.org/10.1016/S0005-1098(01)00074-7