Skip to main content
SpringerLink
Log in

Two fusion predictors for discrete-time linear systems with different types of observations

  • Technical Notes and Correspondence
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

New fusion predictors for linear dynamic systems with different types of observations are proposed. The fusion predictors are formed by summation of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationship between fusion predictors is established. Then, the accuracy and computational efficiency of the fusion predictors are demonstrated on the first-order Markov process and the GMTI model with multisensor environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Y. Bar-Shalom, Ed., Multitarget-Multisensor Tracking: Advanced Applications, Artech House, Norwood, MA, 1990.

    Google Scholar 

  2. Y. Bar-Shalom and X. R. Li, Multitarget-Multisensor Tracking: Principles and Techniques, YBS Publishing, 1995.

  3. Y. M. Zhu, Multisensor Decision and Estimation Fusion, Kluwer, Boston, 2002.

    Google Scholar 

  4. C. Y. Chong, “Hierarchical estimation,” Proc. of the MIT/ONR Workshop on C3, pp. 205–220, 1979.

  5. C. Y. Chong, K. S. Chang, and S. Mori, “Distributed tracking in distributed sensor networks,” Proc. of the American Control Conference, Seattle, WA, pp. 1863–1868, 1986.

  6. C. Y. Chong, S. Mori, and K. S. Chang, “Distributed multitarget multisensor tracking,” in Y. Bar-Shalom, Ed., Multitarget-Multisensor Tracking: Applications and Advances, vol. I, Artech House, Norwood, MA, pp. 247–295, 1990.

    Google Scholar 

  7. Y. M. Zhu, E. B. Song, J. Zhou, and Z. S. You, “Optimal dimensionality reduction of sensor data in multisensor estimation fusion,” IEEE Trans. Signal Processing, vol. 53, no. 5, pp. 1631–1639, 2005.

    Article  MathSciNet  Google Scholar 

  8. Y. Bar-Shalom and L. Campo, “The effect of the common process noise in the two-sensors fused track covariance,” IEEE Trans, Aerospace and Electronics System, vol. 22, no. 11 pp. 803–805, 1986.

    Article  Google Scholar 

  9. X. R. Li, Y. M. Zhu, J. Wang, and C. Z. Han, “Optimal linear estimation fusion — part 1: unified fusion rules,” IEEE Trans. Inf. Theory, vol. 49, no. 9, pp. 2192–2208, 2003.

    Article  Google Scholar 

  10. Y. M. Zhu and X. R. Li, “Best linear unbiased estimation fusion,” Proc. Int. Conf. Multisource-Multisensor Inf. Fusion, Sunnyvale, CA, pp. 1054–1061, 1999.

  11. K. C. Chang, T. Zhi, and R. K. Saha, “Performance evaluation of track fusion with information matrix filter,” IEEE Trans. on Aerospace and Electronics Systems, vol. 38, no. 2, pp. 455–466, 2002.

    Article  Google Scholar 

  12. V. I. Shin, Y. Lee, and T. S. Choi, “Suboptimal linear filtering and generalized Millman’s formula” Proc. of the 6th IASTED International Conference on Signal and Image Processing, Honolulu, USA, pp. 369–374, 1960.

  13. V. Shin, Y. Lee, and T. S. Choi, “Generalized Millman’s formula and its applications for estimation problems,” Signal Processing, vol. 86, no. 2, pp. 257–266, 2006.

    Article  MATH  Google Scholar 

  14. J. Zhou, Y. Zhu, Z. You, and E. Song, “An efficient algorithm for optimal linear estimation fusion in distributed multisensor system,” IEEE Trans. Syst., Man, Cybern, vol. 36, no. 5, pp. 1000–1009, 2006.

    Article  Google Scholar 

  15. S. L. Sun and Z. L. Deng, “Multi-sensor information fusion Kalman filter weighted by scalars for systems with colored measurement noises,” Journal of Dynamic Systems, Measurement and Control, vol. 127, no. 4, pp. 663–667, 2005.

    Article  MathSciNet  Google Scholar 

  16. S. L. Sun, “Multi-sensor optimal information fusion Kalman filters with applications,” Aerospace Science and Technology, vol. 8, no. 1, pp. 57–62, 2004.

    Article  MATH  Google Scholar 

  17. A. T. Alouani, “Distributed estimators for nonlinear systems,” IEEE Trans. Automatic Control, vol. 35, no. 9, pp. 1078–1081, 1990.

    Article  MATH  MathSciNet  Google Scholar 

  18. Z. L. Deng, Y. Gao, L. Mao, Y. Li, and G. Hao, “New approach to information fusion steadystate Kalman filtering,” Automatica, vol. 41, no. 10, pp. 1695–1707, 2005.

    Article  MATH  MathSciNet  Google Scholar 

  19. 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. 1271–1277, 1997.

    Article  Google Scholar 

  20. S. Deb, K. R. Pattipati, and Y. Bar-Shalom, “A Multisensor-Multitarget data association algorithm for heterogeneous sensors,” IEEE Trans. on Aerospace and Electronic Systems, vol. 29, no. 2, pp. 560–568, 1993.

    Article  Google Scholar 

  21. F. L. Lewis, Optimal Estimation with an Introduction to Sthochastic Control Theory, John Willey & Sons, New York, 2001.

    Google Scholar 

  22. S. L. Sun, “Optimal and self-tuning information fusion Kalmman multi-step predictor,” IEEE Trans. on Aerospace and Electronic Systems, vol. 43, no. 2, pp. 418–427, 2007.

    Article  Google Scholar 

  23. Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Willey & Sons, New York, 2001.

    Book  Google Scholar 

  24. H. R. Hashemipour, S. Roy, and A. J. Laub, “Decentralized structures for parallel Kalman filtering,” IEEE Trans. on Automatic Control, vol. 33, no. 1, pp. 88–94, 1988.

    Article  MATH  Google Scholar 

  25. E. Song, Y. Zhu, and J Zhou, “The optimality of Kalman filtering fusion with cross-correlated sensor noises,” Proc. IEEE Conf. Decision and Control, Atlantis, Bahamas, pp. 4637–4642, 2004.

  26. A. T. Alouani, and J. D. Birdwell, “Distributed estimation: constraints on the choice of the local models,” IEEE Trans. on Automatic Control, vol. 33, no. 5, pp. 503–506, 1988.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Mechatronics, Gwangju Institute of Science and Technology, 216 Cheomdangwagiro (Oryong-dong), Buk-Gu, Gwangju, 500-712, Korea

    Ha Ryong Song, Moon Gu Jeon, Tae Sun Choi & Vladimir Shin

Authors
  1. Ha Ryong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moon Gu Jeon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tae Sun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vladimir Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir Shin.

Additional information

Recommended by Editorial Board member Lucy Y. Pao under the direction of Editor Young Il Lee. This work was partially supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MOST), No. R01-2007-000-20227-0 and the Center for Distributed Sensor Network at GIST.

Ha-Ryong Song received the B.S. degree in Control and Instrumentation Engineering from the Chosun University, Korea, in 2006, the M.S. degree in School of Information and Mechatronics from the Gwangju Institute of Science and Technology, Korea, in 2007. He is currently a Ph.D. candidate in Gwangju Institute of Science and Technology. His research interests include estimation, target tracking systems, data fusion, nonlinear filtering.

Moon-Gu Jeon received the B.S. degree in architectural engineering from the Korea University, Korea in 1988. He then received both the M.S. and Ph.D. degrees in computer science and scientific computation from the University of Minnesota in 1999 and 2001, respectively. Currently, he is an Associate Professor at the School of Information and Mechatronics of the Gwangju Institute of Science and Technology (GIST). His current research interests are in machine learning and pattern recognition and evolutionary computation.

Tae-Sun Choi received the B.S. degree in Electrical Engineering from the Seoul National University, Seoul, Korea, in 1976, the M.S. degree in Electrical Engineering from the Korea Advanced Institute of Science and Technology, Seoul, Korea, in 1979, and the Ph.D. degree in Electrical Engineering from the State University of New York at Stony Brook, in 1993. He is currently a Professor in the School of Information and Mechatronics at Gwangju Institute of Science and Technology, Korea. His research interests include image processing, machine/robot vision, and visual communications.

Vladimir Shin received the B.Sc. and M.Sc. degrees in Applied Mathematics from Moscow State Aviation Institute, in 1977 and 1979, respectively. In 1985 he received the Ph.D. degree in Mathematics at the Institute of Control Science, Russian Academy of Sciences, Moscow. He is currently an Associate Professor at Gwangju Institute of Science and Technology, South Korea. His research interests include estimation, filtering, tracking, data fusion, stochastic control, identification, and other multidimensional data processing methods.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, H.R., Jeon, M.G., Choi, T.S. et al. Two fusion predictors for discrete-time linear systems with different types of observations. Int. J. Control Autom. Syst. 7, 651–658 (2009). https://doi.org/10.1007/s12555-009-0416-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-009-0416-0

Keywords

Search

Navigation