# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 3 System Framework and Method
### Sep-RefineNet
Finally, use the convolution kernel of 1 \(\times\) 1 to adjust the number of channels, and add them with the M feature maps before convolution through shortcut to obtain the residual output. Batch normalization layer is used to complete a batch of input data to the same distribution data with average value of 0 and variance value of 1 [24]. It can help the network to converge faster and reduce the over-fitting. The output of Sep-Residual unit is \(X_{n+1}\) in Equation (8). \[X_{n+1}=X_{n}+\sum_{i=1}^{4}F_{i}(X_{n},W_{n}) \tag{8}\]  
where \(F_{i}(X_{n},W_{n})\) is convolution result of \(X_{n}\) in \(i\)-th channel. Sep-Residual unit will better obtain the characteristics of FCM in the case of aliasing pulses and missing pulses, with multiple scales and multiple receptive fields. After being processed by the Sep-Residual unit, it needs to pass the Multi-resolution Fusion, and its structure is shown in Figure 8a. Figure 8: Schematic diagram of the unit structure: (**a**) Multi-resolution Fusion; (**b**) Chained Residual Pooling [25]. Figure 7: Schematic diagram of the Sep-Residual unit: (**a**) original ResNet50 residual block [23]; (**b**) improved Sep-Residual unit; (**b**) multiple channels and multiple receptive fields, which can better extract the characteristics of the signal. The Multi-resolution Fusion fuses the output of the two Sep-Residual units. Through the 3 \(\times\) 3 convolutional layer, the features of the deep stage need to be upsampled and then added to the features of the shallow stage. In this way, deep semantic information can be obtained without losing shallow texture and edge information. The output of Multi-resolution Fusion is the input of Chained Residual Pooling. Through the activation function ReLU and the 5 \(\times\) 5 large pooling kernel on the branch, the attention to the large structural features in FCM is improved. Initial learning rate (LR) sets \(LR=1\times 10^{-4}\), and then, the learning rate of every 20 epochs is reduced to 1/2 of the original until it drops to \(10^{-5}\). The updated method of the learning rate is Equation (9). \[LR=max(lr_{initial}\times 0.5^{\lfloor epoch/20\rfloor},10^{-5}) \tag{9}\]  
In the 100 epochs, the learning rate continues to decline to better complete the training of the model. Considering that labels will be interfered with by noise in the actual situation, label smoothing [26] is introduced, which can prevent the model from over-fitting the label in Equation (10). \[y^{\prime}=(1-\varepsilon)\times y+\frac{\varepsilon}{K} \tag{10}\]  
where \(y\) is original hard label, \(y^{\prime}\) is smoothing label, \(\varepsilon=0.1\) is the acceptable error rate, and \(K=5\) is the number of classifications. The hard label only has 0 or 1. The smoothing label has an acceptable error, reduces the method over-fitting, and makes the signal deinterleaving system have the ability to oppose interference. Due to signal characteristics encoding into FCM, it is determined that the positive and negative samples will have a serious imbalance. Therefore, the loss function in this experiment is the focal loss [27]. Substituting Equation (10) into the focal loss, Equation (11) is obtained. \[focal\ loss=-\sum_{i=0}^{n}\left[\alpha(1-p)\times y^{\prime}logp+(1-\alpha)p^ {\gamma}\times(1-y^{\prime})log(1-p)\right] \tag{11}\]  
The weight coefficient \(\alpha=0.9\) is introduced to adjust the proportion of the positive and negative samples, and the tunable focusing parameter \(\gamma=2\) is used to control the contribution of the difficulty of sample classification to the loss function. Since the number of negative samples is more than the positive samples, if the contribution of negative samples is not suppressed, the contribution will be much higher than the positive samples, which will lead to the poor fitting ability of the model to positive samples. The focal loss can solve this problem, suppress the contribution of negative samples, and pay more attention to samples that are difficult to distinguish and misclassify.