@inproceedings{099bbaa1a26e4f9a805e10fd81bdceb4,
title = "Time-frequency Analysis and Convolutional Neural Network Based Fuze Jamming Signal Recognition",
abstract = "Fuze jamming signal recognition plays a critical role in the battlefield environment. To improve the performance of fuze jamming signals detection, we propose a fuze jamming signal detector based on time-frequency analysis (TFA) and convolutional neural network (CNN), called TFA-CNN, in this paper. The detailed recognition process of the proposed TFA-CNN detector is provided, where the short-Time Fourier trans-form (STFT) is first employed to convert the original jammed fuze signals into the time-frequency images and then the TFA-CNN detector is built to train the recognition model. Simulation results verify that the TFA-CNN detector outperforms the typical existing recognition detectors, such as LeNet, time-frequency images and convolutional neural network (TFI-CNN) and deep neural network (DNN), in the detection performance with a slightly higher time complexity. Specially, the average recognition accuracy of the proposed detector achieves 99.8% even at a low signal-To-interference-plus-noise ratio (SINR).",
keywords = "CNN, STFT, accuracy, fuze, image",
author = "Jikai Yang and Zhiquan Bai and Jiacheng Hu and Yingchao Yang and Zhaoxia Xian and Xinhong Hao and Kyungsup Kwak",
note = "Publisher Copyright: {\textcopyright} 2023 Global IT Research Institute (GiRI).; 25th International Conference on Advanced Communications Technology, ICACT 2023 ; Conference date: 19-02-2023 Through 22-02-2023",
year = "2023",
doi = "10.23919/ICACT56868.2023.10079346",
language = "English",
series = "International Conference on Advanced Communication Technology, ICACT",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "277--282",
booktitle = "25th International Conference on Advanced Communications Technology",
address = "United States",
}