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Preprocessing Using Maximal Autocovariance for Spatio–Temporal Track–Before–Detect Algorithm

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Image Processing and Communications Challenges 5

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 233))

Summary

The detection of local regular patterns and dependent values in heavy noised signal is proposed in this paper. The moving window approach allows computation of the maximal autocovariance of signal. The differences are emphasized using Spatio–Temporal Track–Before–Detect algorithm so tracking such objects is possible. The possibilities of this technique are shown in a few illustrative examples and discussed. The detection of weak signals hidden in the background noise is emphasized.

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Author information

Authors and Affiliations

  1. Department of Signal Processing and Multimedia Engineering, West–Pomeranian University of Technology, Szczecin, 26. Kwietnia 10 Str., 71126, Szczecin, Poland

    Przemysław Mazurek

Authors
  1. Przemysław Mazurek
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Corresponding author

Correspondence to Przemysław Mazurek .

Editor information

Editors and Affiliations

  1. Life Sciences, Institute of Telecommunications, University of Technology, ul.S.Kaliskiego 7, Bydgoszcz, 85791, Poland

    Ryszard S. Choras

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Mazurek, P. (2014). Preprocessing Using Maximal Autocovariance for Spatio–Temporal Track–Before–Detect Algorithm. In: S. Choras, R. (eds) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol 233. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01622-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-01622-1_6

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-01621-4

  • Online ISBN: 978-3-319-01622-1

  • eBook Packages: EngineeringEngineering (R0)

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