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Noise Objects Tracking Using Multiple Order Statistics and Spatio–Temporal Track–Before–Detect Algorithm

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Image Processing and Communications Challenges 8 (IP&C 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 525))

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Abstract

Low SNR object could be tracked and detected using Track–Before–Detect (TBD) algorithms. Most TBD algorithm assume positive signal of object and Gaussian background noise. Multiple order statistics (mean, variance and skewness) for the improving of detection are proposed and analyzed in this paper. Monte Carlo results and the dependence between mean, standard deviation and skewness are provided.

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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, 26. Kwietnia 10 St., 71126, Szczecin, Poland

    Przemysław Mazurek

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  1. Przemysław Mazurek
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Correspondence to Przemysław Mazurek .

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Editors and Affiliations

  1. Institute of Telecommunications and Computer Sciences, UTP University of Science and Technology, Bydgoszcz, Poland

    Ryszard S. Choraś

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Mazurek, P. (2017). Noise Objects Tracking Using Multiple Order Statistics and Spatio–Temporal Track–Before–Detect Algorithm. In: Choraś, R. (eds) Image Processing and Communications Challenges 8. IP&C 2016. Advances in Intelligent Systems and Computing, vol 525. Springer, Cham. https://doi.org/10.1007/978-3-319-47274-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-47274-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47273-7

  • Online ISBN: 978-3-319-47274-4

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