## 3 Open-Area Environmental Interference Suppression Model
### Hybrid Segmentation Algorithm  
For the environmental interference suppression problem, we need to separate different signals for further judgment. The signal segmentation algorithm proposed in this paper includes two parts, U-Net and a connected component analysis [11], which are is shown in the diagram in Figure 3.  
As a typical computer vision problem, segmentation converts the input images into a group of masks with highlighted regions of interest by assigning a category ID pixel-by-pixel according to the object of interest to which it belongs. At present, the most popular segmentation algorithm is based on deep networks, such as VGG [12], Resnet [13], and so on. Due to the existence of down-pooling steps, the standard convolution neural network (CNN) always leads to a loss of spatial resolution and poor segmentation performance [14]. As a result, U-net [15] has been widely applied for segmentation tasks. Figure 3: Hybrid segmentation network model. U-net can be divided into three main parts, which consist of the contracting path, expansive path, and copy and crop. The contracting path is a typical convolutional network with a repetitive structure. Each repetition has two convolution layers (kernel size = \(3\times 3\)) and a max pooling layer with a step size of 2. We double the number of feature channels after each down-sampling. The image dimensions are reduced to \(1/16\) of the original one after four down-samplings. The expansive path consists of four deconvolutional blocks symmetric to the contracting path, with each deconvolution expanding the size of each feature map by two (up-pooling), halving the channel number, and concatenating the corresponding feature map in the contracting path. The four concatenation operations (also called skip connections) merge the deep and shallow feature information [16] (cropping might be necessary to ensure the feature dimensions are consistent). Through the fusion of low-level features and high-level features, the network can retain more high-resolution details and greatly improve the image segmentation accuracy. U-net is trained by using mean square error (MSE) loss, which is defined in this paper as follows:  
\[MSE(y,y^{\prime})=\frac{\sum\limits_{i=1}^{n}{(y_{i}-y^{\prime}_{i})^{2}}}{n}, \tag{1}\]  
where n denotes the number of samples, \(y_{i}\) denotes the true target value, and \(y^{\prime}_{i}\) denotes the predicted value. The gradient of the MSE decreases as the errors decrease and behaves as sound convergence, even with a fixed learning rate. Since U-net uses image tiles for training, the amount of training data is much larger than the number of training images, which makes the network invariant and robust with a small number of samples. Theoretically, the U-net segmentation model can separate different objects in the images. But, for the environmental interference suppression problem, we find it hard for U-net to separate the different signals in the waterfall graph due to the following reasons. Firstly, since the broadcasting and other communication signals are phase-modulated random signals, their time-frequency characteristics in the waterfall graph is similar, i.e., there are almost no texture features for U-net to learn. Secondly, since the signals are volatile, it is hard to construct a stable training dataset for different EMC testing. Fortunately, the signals of different sources always occupy different regions in the time-frequency spectrum, and we can separate different signals via the classical segmentation algorithm based on the connected component analysis. The connected component [17] is generally an image region (Blob) composed of foreground pixels with the same pixel value and adjacent locations in the image. Connected component analysis [18] refers to finding and marking each connected area in an image, which has been widely applied for character segmentation extraction [19], visual tracking [20], and medical image processing [21].