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On-Line State Estimation of Maneuvering Objects by Sequential Monte Carlo Algorithm

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Large-Scale Scientific Computing (LSSC 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2179))

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Abstract

A stochastic sampling algorithm for recursive state estimation of nonlinear dynamic systems is designed and realized in this study. It is applied to the problem of tracking two maneuvering air targets in the presence of false alarms. The performance of the proposed algorithm is evaluated via Monte Carlo simulation. The results show that the nonlinear Bayesian filtering can be efficiently accomplished in real time by simple Monte Carlo techniques.

Partially supported by the Bulgarian National Foundation for Scientific Investigations under grants No I-808/98 and I-902/99.

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Author information

Authors and Affiliations

  1. Central Laboratory for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev St., bl. 25 A, 1113, Sofia, Bulgaria

    D. Angelova, E. Semerdjiev, Tz. Semerdjiev & P. Konstantinova

Authors
  1. D. Angelova
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  2. E. Semerdjiev
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  3. Tz. Semerdjiev
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  4. P. Konstantinova
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Editor information

Editors and Affiliations

  1. Central Laboratory for Parallel Processing, Bulgarian Academy of Sciences, Acad G. Bontchev, 1113, Sofia, Bulgaria

    Svetozar Margenov

  2. DTU, UNI•C, Danish IT Centre for Education and Research, Bldg. 304, 2800, Lyngby, Denmark

    Jerzy Waśniewski

  3. University of Rousse, Studentska 8, 7017, Rousse, Bulgaria

    Plamen Yalamov

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© 2001 Springer-Verlag Berlin Heidelberg

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Angelova, D., Semerdjiev, E., Semerdjiev, T., Konstantinova, P. (2001). On-Line State Estimation of Maneuvering Objects by Sequential Monte Carlo Algorithm. In: Margenov, S., Waśniewski, J., Yalamov, P. (eds) Large-Scale Scientific Computing. LSSC 2001. Lecture Notes in Computer Science, vol 2179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45346-6_11

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  • DOI: https://doi.org/10.1007/3-540-45346-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43043-8

  • Online ISBN: 978-3-540-45346-8

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