Masking of Time-Frequency Patterns in Applications of Passive Underwater Target Detection

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@Article{Sildam:2010:eurasipJASP,
  title =        "Masking of Time-Frequency Patterns in Applications of
                 Passive Underwater Target Detection",
  author =       "Juri Sildam",
  year =         "2010",
  journal =      "EURASIP Journal on Advances in Signal Processing",
  volume =       "2010",
  pages =        "Article ID 298038",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "16876172",
  oai =          "oai:CiteSeerX.psu:10.1.1.396.4388",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.396.4388",
  URL =          "http://asp.eurasipjournals.com/content/pdf/1687-6180-2010-298038.pdf",
  DOI =          "doi:10.1155/2010/298038",
  language =     "eng",
  publisher =    "Hindawi Publishing Corporation",
  size =         "11 pages",
  abstract =     "Spectrogram analysis of acoustical sounds for
                 underwater target classification is used when loud
                 nonstationary interference sources overlap with a
                 signal of interest in time but can be separated in
                 time-frequency (TF) domain. We propose a signal masking
                 method which in a TF plane combines local statistical
                 and morphological features of the signal of interest. A
                 dissimilarity measure D of adjacent TF cells is used
                 for local estimation of entropy H, followed by
                 estimation of {$\Delta$}H=Htc{$-$}Hfc entropy
                 difference, where Hfc is calculated along the time axis
                 at a mean frequency fc and Htc is calculated along the
                 frequency axis at a mean time tc of the TF window,
                 respectively. Due to a limited number of points used in
                 {$\Delta$}H estimation, the number of possible
                 {$\Delta$}H values, which define a primary mask, is
                 also limited. A secondary mask is defined using
                 morphological operators applied to, for example, H and
                 {$\Delta$}H. We demonstrate how primary and secondary
                 masks can be used for signal detection and
                 discrimination, respectively. We also show that the
                 proposed approach can be generalised within the
                 framework of Genetic Programming.",
}

Genetic Programming entries for Juri Sildam

Citations