# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.2.1 Verify the Effectiveness of Sep-RefineNet  
In order to evaluate the ability of feature extraction of Sep-RefineNet and the fitting of the network, the prediction results of the FCM are compared and analyzed. The evaluating indicator is pixel accuracy, which is used to compare each element predicted in FCM with the elements in the label matrix. It can intuitively reflect Sep-RefineNet's ability to fit the FCM. The pixel accuracy is in Equation (12). \[\textit{Pixel accuracy}=\frac{\sum_{i=0}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p _{ij}} \tag{12}\]  
where the numerator of Equation (12) is correctly predicted pixel elements, and the denominator is all predicted pixels. Since FCM has upper triangular redundancy during encoding, only the labeled pixels are considered in the evaluation, and the background pixels are not considered. To verify the ability of learning FCM features for the proposed Sep-RefineNet, select 20 aliased pulse streams from the test set and input them into the trained Sep-RefineNet, and calculate their pixel accuracy. The test results are shown in Table 3.  
Table 3 shows that the proposed Sep-RefineNet can well fit the FCM, and the average pixel accuracy of 20 aliased pulse streams is 97.60%. The image, label, and prediction results of the FCM are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Pulse stream** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Pixel accuracy (\%) & 97.52 & 97.44 & 95.73 & 97.29 & 98.96 & 97.91 & 98.42 & 96.90 & 95.27 & 97.32 \\ \hline
**Pulse stream** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** \\ \hline Pixel accuracy (\%) & 98.89 & 97.57 & 99.04 & 96.13 & 98.65 & 99.54 & 96.89 & 98.58 & 97.39 & 96.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pixel accuracy of 20 pulse streams. Figure 10: The partial enlargement of the FCM results for the test data: (**a**) FCM after pulse stream coding; (**b**) paired label matrix; (**c**) prediction result of FCM. The red arrow is the difference between label matrix and prediction result. From the overall view of Figure 10b,c, Sep-RefineNet can well fit the texture, shape, and structural features of FCM. However, from the details, the direction of the red arrow is the interval between the red and blue dots in the label matrix (b), but it is four red dots connected in the prediction result (c). This could lead to the wrong sorting of the two pulses. However, according to the decoding process in Section 3.1, only the prediction results are symmetric about the diagonal of FCM and will complete the position decoding. The symmetrical position of the red arrow in the prediction matrix is not four connected red dots, which to some extent avoids the wrong sorting of the pulse signal due to the deviation of the prediction result. It improves the accuracy of signal deinterleaving. To verify the effectiveness of the Sep-Residual unit (SRU) and focal loss (FL), the original residual unit (ORU) and cross-entropy (CE) were used to replace the improvements in this paper, and the corresponding model was trained; thus, predict the 20 test pulse streams and calculate their pixel accuracy. In the red solid line in Figure 11, when the residual block is the Sep-Residual unit and the loss function is focal loss, the model has the highest pixel accuracy, which is better than the original residual unit and cross-entropy. The SRU fuses the features on the four channels, which will make the obtained signal features richer. Different sizes of receptive fields can better obtain the overall structural features and local texture features of FCM for convolution. The reason FL is better than CE is due to the characteristics of the FCM. The semantic segmentation task in this paper is not a traditional color image, but a two-dimensional matrix obtained by encoding a one-dimensional pulse signal through FCM.