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@PHDTHESIS{Apfeld:834559,
      author       = {Apfeld, Sabine},
      othercontributors = {Ascheid, Gerd and Heberling, Dirk and Koch, Wolfgang},
      title        = {{M}achine learning for electronic intelligence},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2021-09991},
      pages        = {1 Online-Ressource : Diagramme},
      year         = {2021},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2021},
      abstract     = {Electronic intelligence is concerned with gathering
                      information about radar emitters by intercepting and
                      analysing their signals. By collecting this information,
                      characteristic features are found that can be exploited to
                      recognise known emitters. Traditional analysis and
                      identification approaches rely on databases, which contain
                      descriptions of the radars' operational modes. In
                      conventional radar systems, modes are designed to fulfil a
                      certain function and exhibit constant patterns of the
                      waveform parameters. Agile multifunction radars, however,
                      optimise their waveform parameters for the specific
                      situation and therefore, they do not exhibit constant
                      patterns. Consequently, traditional databases cannot
                      effectively represent their emission characteristics.
                      Moreover, they are unable to efficiently capture the
                      relationships between emissions and hence, they cannot model
                      the emitters' behaviour. This thesis suggests a new emission
                      model, which understands the radar emissions as a language
                      with an inherent hierarchical structure of the five
                      modelling levels letters, syllables, words, commands, and
                      functions. Such an emission model allows exploiting machine
                      learning methods, which are designed for natural language
                      processing and in particular for the field of representation
                      learning. Based on this approach, methods are developed for
                      the four tasks of emission prediction, the identification of
                      the emitter type, the learning of behavioural models, and
                      the recognition of unknown emitters. To this end, predictive
                      models are created that capture the behaviour of agile radar
                      emitters. In addition, several architectures are
                      investigated that combine multiple emitter models into an
                      ensemble. Two contrasting approaches are compared for all
                      four tasks throughout this thesis, namely the "memoryless"
                      Markov chain and the Long Short-Term Memory recurrent neural
                      network, which is designed to "remember" the past. The
                      hierarchical structure of the emission model significantly
                      increases the performance of all considered tasks in
                      comparison to traditional signal analysis methods. These
                      process radar pulses, i.e. letters, based on which emitter
                      identification and recognition of unknown emitters are not
                      successful. Moreover, it is shown that machine learning
                      methods provide large benefits in comparison to conventional
                      approaches in the majority of the settings.},
      cin          = {611810},
      ddc          = {621.3},
      cid          = {$I:(DE-82)611810_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2021-09991},
      url          = {https://publications.rwth-aachen.de/record/834559},
}