International Journal of Control, Automation, and Systems

  • 제4권2호
  • /
  • Pages.165-171
  • /
  • 2006
  • /
  • 1598-6446(pISSN)
  • /
  • 2005-4092(eISSN)
제어로봇시스템학회 (Institute of Control, Robotics and Systems)
권호 표지

A Data Fusion Algorithm of the Nonlinear System Based on Filtering Step By Step

  • Wen Cheng-Lin (College of Automation, Hangzhou Dianzi University) ;
  • Ge Quan-Bo (Department of Electrical Automation, Shanghai Maritime University)
  • 발행 : 2006.04.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes a data fusion algorithm of nonlinear multi sensor dynamic systems of synchronous sampling based on filtering step by step. Firstly, the object state variable at the next time index can be predicted by the previous global information with the systems, then the predicted estimation can be updated in turn by use of the extended Kalman filter when all of the observations aiming at the target state variable arrive. Finally a fusion estimation of the object state variable is obtained based on the system global information. Synchronously, we formulate the new algorithm and compare its performances with those of the traditional nonlinear centralized and distributed data fusion algorithms by the indexes that include the computational complexity, data communicational burden, time delay and estimation accuracy, etc.. These compared results indicate that the performance from the new algorithm is superior to the performances from the two traditional nonlinear data fusion algorithms.

키워드

참고문헌

  1. C. L. Wen and D. H. Zhou, Multiscale Estimate Theory with Application, Tsinghua University Press Inc., Beijing, China, pp. 127-140, 2002
  2. Y. He, G. H. Wang, D. X. Lu etc., Multisensor Information Fusion with Application, Electronic Industry Press, Beijing, China, 2000
  3. C. L. Wen, B. Lu, and Q. B. Ge, 'A data fusion algorithm based on filtering step by step,' Acta Electronica Sinica, vol. 32, no. 8, pp. 1264-1267, 2004
  4. K. C. Chang, R. K. Saha, and Y. Bar-Shalom, 'On optimal track-to-track fusion,' IEEE Trans. on Aerospace and Electronic Systems, vol. 33, no. 4, pp. 1271-1276, 1997 https://doi.org/10.1109/7.625124
  5. X. R. Li, 'Comparison of two measurement fusion methods for Kalman-Filter-Based multisensor data fusion,' IEEE Trans. on Aerospace and Electronic Systems, vol. 37, no. 1, pp. 273-280, 2001 https://doi.org/10.1109/7.913685
  6. S. Blackman, Multiple-target Tracking with Radar Application, Artech House, London, pp. 350-380, 1986
  7. B. S. Rao and H. F. Durrant-Whyte, 'Fully decentralized algorithm for multisensor Kalman filtering,' IEEE Proceedings-D, vol. 138, no. 5, pp. 413-420, 1991
  8. Y. Bar-Shalom, X. R. Li, and T. Kirubarajam, Estimation with Application to Tracking and Navigation, John Wiley & Sons, Inc., New York, 2001
  9. S. Blackman and R. Popoli, Design and Analysis of Modem Tracking Systems, Artech House, London, 1999
  10. L. Hong, 'Centralized and distributed multisensor integration with uncertainties in communication networks,' IEEE Trans. on Aerospace and Electronic Systems, vol. 27, no. 2, pp. 370-379, 1991 https://doi.org/10.1109/7.78311
  11. R. Mahler, 'Random sets: Unification and computation for information fusion-a retrospective assessment,' Proc. of the 7th International Conference on Information Fusion, Sweden, pp. 1-20, 2004
  12. S. Hedvig, 'Multi-target particle filtering for the probability hypothesis density,' Proc. of the 6th International Conference on Information Fusion, Cairns, Australia, pp. 800-806, 2003