DL-based PRI modulation classification and recognition

• Main Author: Tey, Kai Hong
• Other Authors: Teh Kah Chan
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/172489
• Institution: Nanyang Technological University

This report presents a comprehensive investigation into the application of Deep Learning (DL) techniques for the classification and recognition of Pulse Repetition Interval (PRI) modulated signals, with a focus on radar and communication systems. The research involves the signal data simulation, network model implementation, and comparison of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for the automatic detection and classification of diverse PRI modulation techniques across various environmental conditions and interference scenarios. Our findings demonstrate the superiority of DL-based methods over traditional signal processing approaches, shedding light on their interpretability and real-world applicability. In this study, enhancements targeting loss function, network architecture refinement, and the integration of advanced signal processing techniques were proposed, collectively leading to notable performance improvements in the classification and recognition of PRI-modulated signals. Keywords: PRI modulation, Deep Learning, Data Signal Simulation, Convolutional Neural Networks (CNNs), Squeeze-and-Excitation Networks, Focal Loss.