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      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
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      Deep learning in electronic warfare systems: automatic pulse detection and intra-pulse modulation recognition

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      Author(s)
      Akyon, Fatih Cagatay
      Advisor
      Arıkan, Orhan
      Date
      2020-12
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
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      Abstract
      Detection and classification of radar systems based on modulation analysis on pulses they transmit is an important application in electronic warfare systems. Many of the present works focus on classifying modulations assuming signal detection is done beforehand without providing any detection method. In this work, we propose two novel deep-learning based techniques for automatic pulse detection and intra-pulse modulation recognition of radar signals. As the first nechnique, an LSTM based multi-task learning model is proposed for end-to-end pulse detection and modulation classification. As the second technique, re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Another major issue on this area is the training and evaluation of supervised neural network based models. To overcome this issue we have developed an Intentional Modulation on Pulse (IMOP) measurement simulator which can generate over 15 main phase and frequency modulations with realistic pulses and noises. Simulation results show that the proposed FFCNN and MODNET techniques outperform the current stateof-the-art alternatives and is easily scalable among broad range of modulation types.
      Keywords
      Intra pulse modulation
      Electronic warfare
      Convolutional neural network (CNN)
      Long short term memory (LSTM)
      Deep learning
      Machine learning
      Multi task learning
      Simulator
      Feature fusion
      Time frequency analysis
      Robust least squares
      Pulse detection
      Modulation classification
      Waveform recognition
      Sincnet
      Energy detector
      Autoencoder
      Permalink
      http://hdl.handle.net/11693/54868
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      • Dept. of Electrical and Electronics Engineering - Master's degree 620
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