Deep learning in electronic warfare systems: automatic pulse detection and intra-pulse modulation recognition
Author(s)
Advisor
Arıkan, OrhanDate
2020-12Publisher
Bilkent University
Language
English
Type
ThesisItem 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 modulationElectronic 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