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International Conference on Computational Science

ICCS 2019: Computational Science – ICCS 2019 pp 60–70Cite as

Monte Carlo Analysis of Local Cross–Correlation ST–TBD Algorithm

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11536)

Abstract

The Track–Before–Detect (TBD) algorithms allow the estimation of the state of an object, even if the signal is hidden in the background noise. The application of local cross–correlation for the modified Information Update formula improves this estimation for extended objects (tens of cells in the measurement space) compared to the direct application of the Spatio–Temporal TBD (ST–TBD) algorithm. The Monte Carlo test was applied to evaluate algorithms by using a variable standard deviation of the additive Gaussian noise. The proposed solution does not require prior knowledge of the size or measured values of the object. Mean Absolute Error for the proposed algorithm is much lower, close to zero to about 0.8 standard deviation, which is not achieved for the ST–TBD.

Keywords

  • Track–Before–Detect
  • Tracking
  • Algorithm analysis
  • Monte Carlo
  • Cross–correlation

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  • DOI: 10.1007/978-3-030-22734-0_5
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Acknowledgment

This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 “Szczecin University of Technology – Research and Education Center of Modern Multimedia Technologies” (Poland).

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Authors and Affiliations

  1. Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology Szczecin, Szczecin, Poland

    Przemyslaw Mazurek & Robert Krupinski

Authors
  1. Przemyslaw Mazurek
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  2. Robert Krupinski
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Corresponding author

Correspondence to Przemyslaw Mazurek .

Editor information

Editors and Affiliations

  1. University of Algarve, Faro, Portugal

    Dr. João M. F. Rodrigues

  2. University of Algarve, Faro, Portugal

    Dr. Pedro J. S. Cardoso

  3. University of Algarve, Faro, Portugal

    Dr. Jânio Monteiro

  4. University of Algarve, Faro, Portugal

    Prof. Roberto Lam

  5. University of Amsterdam, Amsterdam, The Netherlands

    Dr. Valeria V. Krzhizhanovskaya

  6. University of Amsterdam, Amsterdam, The Netherlands

    Michael H. Lees

  7. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  8. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Mazurek, P., Krupinski, R. (2019). Monte Carlo Analysis of Local Cross–Correlation ST–TBD Algorithm. In: , et al. Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science(), vol 11536. Springer, Cham. https://doi.org/10.1007/978-3-030-22734-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-22734-0_5

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