Advertisement

About the Specifics of the IMM Algorithm Design

  • Iliyana Simeonova
  • Tzvetan Semerdjiev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2542)

Abstract

It is well known, that interacting multiple model (IMM) state estimation algorithm is one of the most cost-effective filters for tracking maneuvering targets. The present paper is related to the specifics of the IMM algorithm design. It combines the results, conclusions and experience of different authors considered in their papers. The results discussed and depicted here are root mean square errors and the filters ability to distinct the various flight phases. This paper helps the air traffic control experts fast and easy to make a decision which IMM configuration is suitable for a given problem.

Keywords

Process Noise Turn Rate Civilian Aircraft Aircraft Motion Maneuvering Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bar-Shalom, Y., Li, X. R.: Multitarget-multisensor tracking: principles and techniques. Storrs, CT: YBS Publishing (1995)Google Scholar
  2. 2.
    Bar-Shalom, Y., Li, X. R.: Estimation and tracking: principles,techniques and software. Artech House, Boston, MA (1993)Google Scholar
  3. 3.
    Angelova, D., Jilkov, V., Semerdjiev, Tz.: State estimation of a nonlinear dynamic system by parallel algorithm. In: Proc. EUROMECH-2nd EUropean Nonlinear Oscilation Conf., Prague, September 9–13, (1996) 215–218Google Scholar
  4. 4.
    Munir, A., Atheron D.: Maneuvering target tracking using different turn rate models in the interacting multiple model algorithm. In: Proc. of the 34th Conf. on Decision and Control, New Orleans, LA-Desember (1995) 2747–2751Google Scholar
  5. 5.
    Bar-Shalom, Y.:Multitarget-Multisensor Tracking: Aplications and Advances, Volume II. Artech House, Boston, MA (1993)Google Scholar
  6. 6.
    Lero, D., Bar-Shalom Y.: Interactive Multiple Model Tracking with Target Amplitude Features. IEEE Trans. Aerospace and electronic systems. AES-29 (1993) 495–508Google Scholar
  7. 7.
    Angelova, D., Jilkov, V., Semerdjiev, Tz.: Tracking Maneuvering Target by Interacting Multiple model. Comtes rendus de l’Academie bulgare des Sciences Tome 49 (1996) 37–40Google Scholar
  8. 8.
    Herrero, J., Portas, J., López, J., Vela, Garcia, J.: Interactive Multiple Model Filter Optimisation Tool for Air Traffic Control Applications. In Proc.Fourth Annual Conf. on Information Fusion, Montréal, Québec, Canada (2001) TuB2–19–TuB2–26Google Scholar
  9. 9.
    Mazor, E., Averbuch, A., Bar-Shalom Y., Dayan, J.: Interacting Multiple Model Methods in Target Tracking: A Survey. IEEE Trans. Aerospace and electronic systems AES-34 (1998) 103–123CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Iliyana Simeonova
    • 1
  • Tzvetan Semerdjiev
    • 1
  1. 1.Central Laboratory for Parallel ProcessingBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations