Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks. Akyon, F. C., Alp, Y. K., Gok, G., & Arikan, O. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 2290-2294, Sep., 2018.
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Paper doi abstract bibtex
Paper doi abstract bibtex 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.
@InProceedings{8553176,
author = {F. C. Akyon and Y. K. Alp and G. Gok and O. Arikan},
booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
title = {Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks},
year = {2018},
pages = {2290-2294},
abstract = {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.},
keywords = {electronic warfare;feature extraction;feedforward neural nets;learning (artificial intelligence);phase modulation;pulse modulation;radar signal processing;radar signals;electronic warfare systems;deep-learning based technique;intra-pulse modulation types;measured radar signal;instantaneous phases;training multiple convolutional neural networks;phase modulated signals;re-assigned spectrogram;feature fusion-based convolutional neural network;Frequency modulation;Feature extraction;Signal to noise ratio;Convolutional neural networks;Training;Radar;Phase modulation},
doi = {10.23919/EUSIPCO.2018.8553176},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437047.pdf},
}Downloads: 0
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