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      • Department of Electrical and Electronics Engineering
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      Deep learning for radar signal detection in electronic warfare systems

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      Author(s)
      Nuhoglu, M. A.
      Alp, Y. K.
      Akyön, Fatih Çağatay
      Date
      2020
      Source Title
      IEEE National Radar Conference
      Publisher
      IEEE
      Language
      English
      Type
      Conference Paper
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      Abstract
      Detection of radar signals is the initial step for passive systems. Since these systems do not have prior information about received signal, application of matched filter and general likelihood ratio tests are infeasible. In this paper, we propose a new method for detecting received pulses automatically with no restriction of having intentional modulation or pulse on pulse situation. Our method utilizes a cognitive detector incorporating bidirectional long-short term memory based deep denoising autoencoders. Moreover, a novel loss function for detection is developed. Performance of the proposed method is compared to two well known detectors, namely: energy detector and time-frequency domain detector. Qualitative experiments show that the proposed method is able to detect presence of a signal with low probability of false alarm and it outperforms the other methods in all signal-to-noise ratio cases.
      Keywords
      Passive systems
      Detection
      Deep learning
      Longshort term memory
      Autoencoder
      Permalink
      http://hdl.handle.net/11693/55010
      Published Version (Please cite this version)
      https://dx.doi.org/10.1109/RadarConf2043947.2020.9266381
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      • Department of Electrical and Electronics Engineering 3868
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