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).
Original language | English |
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Title of host publication | 25th International Conference on Advanced Communications Technology |
Subtitle of host publication | New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic!!, ICACT 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 277-282 |
Number of pages | 6 |
ISBN (Electronic) | 9791188428106 |
DOIs | |
Publication status | Published - 2023 |
Event | 25th International Conference on Advanced Communications Technology, ICACT 2023 - Pyeongchang, Korea, Republic of Duration: 19 Feb 2023 → 22 Feb 2023 |
Publication series
Name | International Conference on Advanced Communication Technology, ICACT |
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Volume | 2023-February |
ISSN (Print) | 1738-9445 |
Conference
Conference | 25th International Conference on Advanced Communications Technology, ICACT 2023 |
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Country/Territory | Korea, Republic of |
City | Pyeongchang |
Period | 19/02/23 → 22/02/23 |
Keywords
- CNN
- STFT
- accuracy
- fuze
- image