# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 3 System Framework and Method
### Sep-RefineNet  
Feed the encoded FCM and paired label matrix to a separable refinement network (Sep-RefineNet) to extract semantic features. The definition of semantics is the characteristic relationship between the elements formed by the PRI of the pulse sequence in the FCM. It includes texture, shape, and structure formed by different PRI modulation modes, aliasing pulses, and missing pulses. The context information of the pulse sequence represents that each FCM element is not isolated but has a semantic relationship with the surrounding elements and potential PRI, which determines the texture, shape, and structural features presented. Figure 5: Visualization image of FCM and label matrix: (**a**) encoded FCM visualization image; (**b**) partial enlargement of the FCM; (**c**) visualization image of the corresponding label matrix; (**d**) partial enlargement of the label matrix. As the depth of the network increases, problems such as loss of feature information, gradient vanishing, and gradient explosion will inevitably occur in traditional deep learning networks. It makes the deep semantic information of FCM difficult to extract. We designed the Sep-Residual unit with channel separation and multi-receptive fields, multi-resolution fusion, and chained residual pooling, and built a deep learning network that can fuse shallow and deep semantic information. Identify and segment the FCM to complete the radar signal deinterleaving. The designed Sep-RefineNet is presented in Figure 6.  
First, the FCM is input. The Pre Conv block is three \(3\times 3\) convolutional layers and a pooling layer. The step size of the first convolutional layer is two, which adjusts the feature map size, and the next two consecutive convolutional layers have a step size of 1, which is used to extract features. The max pooling layer resizes the feature map and outputs the largest feature element. The backbone is ResNet50 [23], which is used to extract the features of FCM. It outputs four stages on different resolutions, which are 1/4, 1/8, 1/16, and 1/32 the size of the original resolution. Then, these four stages will pass through a Sep-RefineNet block, and the output of the fourth stage will complete the Sep-Residual unit with the output of the third Stage. Then, multi-resolution fusion and chained residual pooling are used for feature fusion. The output of the third stage will also extract features through the same Sep-RefineNet as the output of the second stage. By analogy, the output to the last Sep-RefineNet block is the input of the Output Conv. The Output Conv is three concatenated convolutional layers: the first two layers are used for feature extraction and channel number adjustment, and the last layer is used to map to the predicted label of FCM. The deep feature layer has more information such as the texture and structure of the FCM, and the shallow feature layer has more edge and detail information. Sep-RefineNet has good capabilities for FCM feature extraction, feature fusion and semantic segmentation. Figure 7 is the Sep-Residual unit. Figure 6: The network structure of Sep-RefineNet. The three parameters of the rectangular box in Figure 7 are channel of input, kernel size of convolution, and channel of output. The number of input channels is M, and the features extracted by the Sep-Residual unit are added to obtain the output. In the Sep-residual unit, the first layer uses a convolution kernel size with 1 \(\times\) 1, adjusts the number of channels, and obtains the M/16 output channels. Leaky ReLu: \(x=max\{0.01x,x\}\) is an activation function to improve the nonlinear ability of network fitting. Then, the receptive fields of different sizes are obtained, which are 3, 5, 7, and 9, respectively. It can enrich the extraction characteristics of the encoding FCM. Then, concatenate the four branches with M/16 channels to obtain M/4 feature maps.