Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks

Autor:	Akyon, Fatih Cagatay, Alp, Yasar Kemal, Gok, Gokhan, Arikan, Orhan
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Zdroj:	2018 26th European Signal Processing Conference (EUSIPCO)
Druh dokumentu:	Working Paper
DOI:	10.23919/EUSIPCO.2018.8553176
Popis:	Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. 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. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types. Comment: Published at EUSIPCO2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.09834 Zobrazit plný text záznamu View this record from Arxiv