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IEEE/ICACT20230135 Slide.16        [Big Slide]       [YouTube]	Oral Presentation
	And finally, the conclusion. we propose a fuze jamming signal detector based on time-frequency analysis and convolutional neural network (TFA-CNN). First, the original jammed fuze signal is converted into a time-frequency image by pre-processing, and the jamming signal features can be extracted in the time and frequency domains jointly. Then, the time-frequency image is input to the TFA-CNN detector for jamming signal recognition. Simulation results have shown that the proposed detector can achieve excellent recognition accuracy in both low and high SINR regions. Moreover, the proposed detector outperforms the existing TFI-CNN, LeNet, and DNN detectors and achieves a better balance between recognition accuracy and model complexity.

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