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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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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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Correspondence to Przemyslaw Mazurek .

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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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