Automatic Radar Modulation Classification
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Automatic modulation classification is concerned with identifying the modulation present on a radio wave. This can be any type of radar or communication signal. It is employed in fields such as cognitive radio for communications, radar analysis for electronic warfare.This thesis is dedicated to classifying a variety of modulations used in modern radar. These include unmodulated, various types of frequency modulation, and phase shift keyed waveforms. This task is accomplished through feature extraction and machine learning techniques. The objective is to determine a suitable method applicable for real-time implementation in a complex electronic warfare environment.Three techniques are proposed: a decision tree combined with Multilayer Perceptron Neural Network, a Multilayer Perceptron Neural Network, and a Convolutional Neural Network. The simulation results show that the decision tree achieves a low classification performance, the Multilayer Perceptron achieves good results in a controlled environment, while the Convolutional Neural Network achieves generalizable results.
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Copyright © 2018 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2018
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fahlman-automaticradarmodulationclassification.pdf | 2023-05-05 | Public | Download |