# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 3 Proposed Method
### Iterative Update of Target Category Confidence  
Based on the network classification model of each round, the category membership of the target can be obtained. If the network classification model is not trained, the category membership of the target will be randomly initially given. Then, the target category is updated through the correction fusion of the belief function, and the sample data after updating the category label can be used for classifier training and parameter optimization. The optimized classifier can also be reused to obtain the category membership of the target. This cycle continues until the category labels of each point in the target dataset are no longer updated or iterated  
Step 2: In each round of iterative updates, the samples in subset \(\Phi_{1}\) can be temporarily used as training sample data \(\big{\{}(O_{i},Y_{i}),i=1,\cdots,N_{\Phi_{1}}\big{\}}\) after being classified by the deep learning network model. \(N_{\Phi_{1}}\) is the total number of samples in dataset \(\Phi_{1}\), and \(Y_{i}\) is the category label of target \(O_{i}\). Then, by combining the mass function of each sample, the center of this pattern category can be obtained as follows.  
\[m_{\text{C}_{q}}=\sum_{\text{C}_{q}\subseteq\{C_{o},C_{n}\},O_{i}\in\Phi_{1}}m _{t}(O_{i})/N_{\Phi_{1}} \tag{24}\]  
Step 3: For the sample \(O_{j}\) in subset \(\Phi_{2}\), the confidence similarity \(d\) between it and the center of categories \(C_{o}\) and \(C_{n}\) is calculated separately.  
\[d(O_{j},C_{q})=\Big{\|}m_{t}(O_{j})-m_{\text{C}_{q}}\Big{\|},\ \text{C}_{q}\in\{C_{o},C_{n}\} \tag{25}\]  
According to the value of confidence similarity, the sample data are sequentially assigned to the pattern categories with the closest confidence.  
In RPREC, the iterative update process of target category confidence is shown in Figure 4.  
Based on the above implementation process, it can be seen that easily classified plots can provide additional evidence to help classify uncertain plots, especially in cases where clutter and target plots have high similarity in the feature space. This is also the advantage of the iterative optimization classification strategy.We summarize the RPREC in Algorithm 1.  
Figure 4: The flowchart for implementing category updates.  
```
0: Radar data set \(O=\{o_{1},\cdots,o_{n}\}\); Classification threshold \(T1\) and \(T2\); Deep learning classifier; Number of Decision Evidence \(K\); Maximum number of iteration updates \(Th\); Initialization: Set the number of network layers and nodes in each layer. If there are samples with class labels, the classifier is initially trained based on recognition framework \(\Omega=\{C_{o},C_{n},\Theta\}\).
1: s \(\leftarrow\) 0;
2:repeat
3: Calculate the category membership \(\mu_{i}=(\mu_{i1},\cdots,\mu_{ic})\) of each target \(O_{i}\);
4: Calculate the total belief of each category: \(Bel(C_{o})\), \(Bel(C_{n})\), \(Bel(\Theta)\).
5: Calculate the number \(K_{i}\) of samples that should be selected in each pattern category based on Equation (11), and build the decision evidence set \(\Phi_{K}(i)\) of each target \(O_{i}\);
6: Calculate the confidence similarity \(M_{ij}\) between the target data and the decision sample;
7: Build the basic confidence assignment function \(m_{i}\) for each sample;
8: Combine the evidence in the decision evidence subset \(T_{q}\) with the same category, and obtain fusion confidence assignment function \(m_{t}^{T_{q}}\);
9: Calculate the evidence discount factor \(W_{C_{q}}\), and Obtain correction evidence \(dm_{t}^{T_{q}}(C_{q})\);
10: Combine the correction evidence and obtain the global mass function \(m_{t}\);
11: Calculate the confidence level \(Bel\) of target \(O_{i}\) belonging to each pattern category;
12: Update category labels for each target data based on classification rules;
13: s \(\leftarrow\) s + 1;
14:until s = \(Th\) or the category labels of each target is no longer updated;
15:return the category assignment relationship of each target \(\{m(O_{i},C_{q})\}_{i=1}^{N}\)
```  
**Algorithm 1**RPREC Algorithm.