# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 4 Experiment
### Experiments and Results Analysis
Therefore, we can conclude that the final model proposed in this paper has an overall improved performance in detection probability compared to the baseline model and Baseline + EIOU model and has higher robustness and improved performance in detection probability at low SNR compared to the Baseline + BiFPN model. Figure 12 shows the comparison of the false alarm probability (a) and the average error of the signal parameters (b) obtained for the models at different stages of this paper with different signal-to-noise ratios of the test set. Similar to Figure 11, we also compare the performance of the four neural network models at different stages. First, we can find that the false alarm probability and the average error of signal parameters decrease as the SNR increases. Then, in the comparison of the false alarm probability, the Baseline + BiFPN module has the highest, which proves to be the worst performance in this regard. This shows that although the detection probability is improved by adding the BiFPN module, the false alarm probability also increases. Moreover, the Baseline + EIOU model clearly causes a lower false alarm probability than the Baseline + BiFPN model. The addition of EIOU effectively reduces the false alarm probability in the low SNR case, as well demonstrated in Figure 12. Therefore, the suppression of false alarm probability by EIOU is evident, and we proceeded to analyze the results of the final BiFPN + EIOU improved model on this basis. On average, the final method proposed in this paper has the lowest one, which proves to be the best performance in this regard. In other words, the final method can improve the increased false alarm probability with the addition of the BiFPN module and has excellent performance. Finally, in the comparison of the average error of signal parameters, we find that the final method proposed in this paper is the lowest and the baseline method is the highest. The BiFPN improved model and the EIOU improved model are in the middle of this performance, and the data for both are very similar, leading to almost overlapping curves. Compared with the baseline method, the final method proposed in this paper shows the greatest improvement at 0 dB, with a reduction in about 1%. In the range of SNR of 0-16 dB, the average error of signal parameters is reduced by about 1-0.25%, which can prove its excellent performance. Figure 11: Experimental results of detection probability under different signal-to-noise ratios. Finally, we conducted comparison experiments among the proposed method with the FRCNN method to demonstrate that our model still has advantages in the face of two-stage target detection models. The results are shown in Table 3. It is worth mentioning that due to the large arithmetic power required by FRCNN, we generated an additional training set consisting of 2000 time-frequency images, and the other parameters of the dataset are kept consistent. In the experiments, the FRCNN and our method used the same training dataset. With the assurance of model convergence, we used mAP to measure the effectiveness of the model. Because of the huge disparity in mAP, we did not evaluate these two models with other metrics anymore. In addition, we also tested the mAP of the SSD model [18] and compared its results with the results of the model proposed in this paper. Obviously, by comparing the performance of mAP with SSD and FRCNN, we conclude that our model works much better than FRCNN and SSD.