# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.2.2 Verify the Effectiveness of Deinterleaving  
The test pulse streams are fed into the network to verify the effectiveness of the proposed FCM and Sep-RefineNet for deinterleaving and are compared with traditional deinterleaving algorithms CDIF, SDIF, and PRI transformation. The threshold function of CDIF is Equation (1), and the adjustable coefficient k \(<\) 1. The threshold function of SDIF is Equation (2), and the adjustable coefficient \(\alpha\)\(\in\)[0, 1] is used to control the bending and translation of the threshold curve, and \(k\) is an empirical parameter. Equations (3)-(6) are PRI transformations, which screen potential PRI according to the rules in [16]. Figure 12 is SDIF's estimation of potential PRI for different pulse streams using the same set of manual parameters \(\alpha\) and \(k\).  
Due to the same manual threshold, parameters \(\alpha\) and \(k\) are used in different pulse streams, the potential PRI of (b) is missed, which will lead to the missing selection in pulse sequence search. For Figure 12b, if the manual parameter \(k\) is increased and \(\alpha\) is set appropriately so that the threshold function bends and translates downward, the missed selection of this PRI can be avoided. It reflects that the SDIF relies too much on the manual experience.  
In order to evaluate the algorithm's ability of deinterleaving, recall and precision are introduced. The smaller the recall, the more serious the signal omission. The smaller the precision, the more serious the signal error sorting. The F1-Score is an indicator for the comprehensive evaluation of recall and precision. It is a comprehensive reflection of the algorithm's ability of deinterleaving signals. It is defined as follows.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental** & **Sep-** & **U-Net** & **Deeplab-v2** & **RefineNet** & **PSNet** \\
**Network** & **RefineNet** & **U-Net** & **Deeplab-v2** & **RefineNet** & **PSNet** \\ \hline Mean pixel accuracy (\%) & 97.60 & 95.85 & 94.27 & 94.76 & 94.16 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The prediction results of different semantic segmentation networks.  
\[recall=\frac{TP}{TP+FN} \tag{13}\]  
\[presicion=\frac{TP}{TP+FP} \tag{14}\]  
\[F1-Score=2\times\frac{recall\times precision}{recall+precision} \tag{15}\]  
where TP is the predicted positive sample and represents the correctly sorted pluses. FP represents positive samples that are wrongly predicted, which will lead to false alarms. FN is a negative sample of the wrong prediction, which will lead to the missed selection. Figure 13 is a comparison of the F1-Score of Sep-RefineNet and the traditional deinterleaving algorithms.  
Figure 12: The results of traditional SDIF algorithm sorts potential PRI. The blue line represents the potential PRI, which is estimated by the histogram statistics. The red curve represents the threshold function, and its degree of curvature and position translation are adjusted by manual parameters \(\alpha\) and \(k\) in Equation (2). When the potential PRI exceeds the threshold function, this PRI is selected for pulse sequence search to complete signal deinterleaving. (**a**) The SDIF algorithm successfully sorts out potential PRI; (**b**) the potential PRI does not exceed the threshold, which means this potential PRI sorting failed.  
Figure 13: Comparison of Sep-RefineNet and traditional deinterleaving algorithms.  
The F1-Score of the proposed method is better than traditional algorithms in terms of accuracy and stability for radar signal deinterleaving. For CDIF and SDIF, when tuning the threshold, it is necessary to estimate the pulse stream based on experience to more suitably use threshold judgment and pulse sequence search to sort pulses. In our method, after the difference order, network structure, and loss function are set in the above experiments, there is little human intervention in the deinterleaving process. PRI is encoded by FCM, then Sep-RefineNet extracts and segments the FCM, and finally completes signal deinterleaving intelligently.