# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 5 Experiment, Results, and Analysis
### Results, Discussions, and Analysis  
To explicitly explore the recognition accuracy of CNN-ST and conduct the comparative analyses, Figure 5 illustrates the overall recognition accuracies of CNN-ST, CNN-DQLN, and CNN-Softmax as a function of SNR. Therein, the testing dataset used in this subsection represents the dual-component radar signals under the same SNR, and the SNR used ranges from \(-10\) dB to \(10\) dB. Figure 5 demonstrates that an increase in SNR leads first to increasing overall recognition accuracies of CNN-ST, CNN-DQLN, and CNN-Softmax, and then leveling off. Therein, the increasing trend for CNN-ST at the lower SNR is faster than CNN-DQLN and CNN-Softmax, and achieves better recognition performance over others. The recognition performance of CNN-ST, CNN-DQLN, and CNN-Softmax are 50.88%, 37.37%, and 26.49%, at \(-10\) dB, respectively. The recognition performance of CNN-ST is 13.51% higher than that of CNN-DQLN, and 24.39% higher than that of CNN-Softmax. The recognition accuracy of CNN-ST is 82.58% at \(-8\) dB, while it is 74.74% and 57.02% for CNN-DQLN and CNN-Softmax, respectively. Moreover, the recognition accuracies can reach 100%, 98.77%, and 95.44% at 4 dB for CNN-ST, CNN-DQLN, and CNN-Softmax, respectively. Therefore, CNN-ST achieves superior recognition accuracy compared to CNN-DQLN and CNN-Softmax. The reason is that the adopted swim transformer in the CNN-ST can extract the more detailed features of the radar signals. To clearly express the recognition performance of each type of radar signal, Figure 6 illustrates the variation in the recognition accuracy of eight types of signals with SNR. Figure 6 demonstrates that the recognition accuracies of eight types of signals increase with increasing SNR, while the increasing trend levels off at the higher SNR. It means that the lower SNR exerts an important effect on recognition accuracy. Moreover, 2 FSK and LFM demonstrate a superior recognition performance over the others. That is because the clearer the TFIs for 2 FSK and LFM are, the easier the detailed features are extracted, and the better the recognition performance which can be obtained. The recognition accuracies of 2 FSK, EQFM, 4 FSK, BPSK, LFM, SFM, NS, and FRANK are 90.83%, 82.14%, 77.98%, 67.62%, 80.71%, 75.95%, 73.45%, and 71.91%, at SNR of \(-10\) dB, respectively. Moreover, the recognition accuracies of all types of radar signals are basically up to 1 at larger than 0 dB. Therefore, the designed CNN-ST model demonstrates better recognition performance at the same SNR. Figure 5: Overall recognition accuracies of CNN-ST, CNN-DQLN, and CNN-Softmax as a function of SNR. To investigate the influence of different SNRs on the recognition accuracy of dual-component signals, Figure 7 demonstrates the recognition accuracy as a function of different SNRs. Therein, the SNR is \(-12\) dB to \(4\) dB; the X and Y axes represent the SNR under different radar signals; and the Z axis represents the classification accuracy. The dual-component radar signals include the following: overlapping 2 FSK and 4 FSK (2 FSK-4 FSK), overlapping 2 FSK and BPSK (2 FSK-BPSK), overlapping 2 FSK and EQFM (2 FSK-EQFM), overlapping 2 FSK and FRANK (2 FSK-FRANK), overlapping 2 FSK and LFM (2 FSK-LFM), overlapping 2 FSK and NS (2 FSK-NS), overlapping 2 FSK and SFM (2 FSK-SFM), overlapping LFM and 4 FSK (LFM-4 FSK), overlapping LFM and BPSK (LFM-BPSK), overlapping LFM and EQFM (LFM-EQFM), overlapping LFM and FRANK (LFM-FRANK), and overlapping LFM and SFM (LFM-SFM). Figure 7 shows that the recognition accuracies of twelve types of randomly overlapping dual-component radar signals increase as different SNRs increase. When the different SNRs of the dual-component radar signal are higher than \(-2\) dB, the increasing trend gradually levels off, and the recognition accuracies are up to \(1\). It means that the different SNRs of the dual-component radar signal exert less effect on the recognition accuracy, especially at the higher SNR.