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Fig. 6 demonstrates the recognition performance of the proposed TFA-CNN and typical LeNet, TFI-CNN, and DNN detectors. It is noticed that the recognition accuracy of the proposed TFA-CNN detector is obviously better than that of the other detectors, especially in the low SINR region. For 15(fifteen) jamming types, the TFA-CNN detector can achieve an average recognition accuracy of 99.8%(ninety-nine point eight percent) in all the SINR regions. The average recognition accuracy of the DNN detector gets the lowest since the DNN detector mainly performs global feature extraction at each layer during the training process and does not consider the structural characteristics of the images. In contrast, the CNN detector considers the contextual information of the features within each convolution, and the integration of features occurs slowly and orderly. Therefore, the CNN detector achieves an advantage over the DNN detector in image processing. The LeNet and TFI-CNN detectors perform poorly in the low SINR region because the structure of these two detectors is not sufficient to learn the features of all jamming signals, which degrades the average recognition accuracy. |
IEEE/ICACT20230135 Slide.14
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