# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Design and Update of Confidence Classification Network
#### 3.4.1 Classifier Design  
Deep learning networks have unique advantages in data classification. They can extract the deep characteristics of samples through layer-by-layer networks, thereby achieving the separation in high-dimensional feature spaces. Considering that radar plots do not have the same complexity as image information, it is not necessary to use complex network structures such as convolutional neural networks and recurrent neural networks. Therefore, a fully connected neural network with more efficient uncertain data processing capabilities is designed in this paper.  
The specific design of this confidence classification network is shown in Figure 4. Firstly, the radar plot features are received through the input layer. Then, deep feature mining is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mass Function** & \(m_{T}\) & \(m_{C}\) & \(m_{T,C}\) & \(dm_{I}\) & \(m_{G}^{\Phi_{K}}\) \\ \hline \(m(\omega_{T})\) & 0.98 & 0 & 0.235 & 0.355 & 0.391 \\ \(m(\omega_{c})\) & 0 & 0.76 & 0.015 & 0.471 & 0.363 \\ \(m(\Theta)\) & 0.02 & 0.24 & 0.75 & 0.174 & 0.246 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results obtained by combining evidences from each step.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mass Function**} & \multicolumn{5}{c}{\(K\) Nearest Neighbor Evidence**} \\ \cline{2-5}  & \#1 & \#2 & \#3 & \#4 & \#5 & \#6 \\ \hline \(m(\omega_{T})\) & 0.8 & 0 & 0.9 & 0 & 0 & 0 \\ \(m(\omega_{c})\) & 0 & 0.5 & 0 & 0.4 & 0.2 & 0 \\ \(m(\Theta)\) & 0.2 & 0.5 & 0.1 & 0.6 & 0.8 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evidence needs to be combined in Example 2.  
carried out through hidden layers. Finally, in the output layer, the membership degree of radar plots belonging to target, clutter, and uncertainty is given. The specific parameter configurations for each layer of this confidence classification network are as follows:  
Input layer: Before being entered into the confidence network classifier, all radar plots need to undergo feature extraction and normalization processing as described in Section 3.1. Therefore, the number of input layer nodes corresponds to the dimension of normalized features, which is set to 10.  
Hidden layer: The design of hidden layers usually considers two factors: the size of radar plots and the distribution of samples in the feature space. After some exploratory data analysis experiments in the early stage, setting the hidden layer at 10 layers with 30 nodes in each layer has a good effect. ReLU, Sigmoid, and Tanh functions are the most commonly used activation functions. ReLU has a fast convergence speed, so it is chosen to activate each hidden layer.  
Output layer: In the output layer, it is necessary to provide the membership degree of radar plots belonging to target, clutter, and uncertainty. So, the number of output layer nodes is set to three. We use \(\mu_{\omega_{T}}\), \(\mu_{\omega_{c}}\), and \(\mu_{\Theta}\) to represent the class membership, which can be seen as the probability that each radar plot belongs to target, clutter, and uncertainty. They satisfy the relationship that \(\mu_{\omega_{T}}+\mu_{\omega_{c}}+\mu_{\Theta}=1\).