# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Design and Update of Confidence Classification Network
#### 3.4.2 Classifier Optimization  
Let \(\Phi_{Train}=\{(X_{i},\omega_{i}),i=1,\cdots,N_{T}\}\) be the training set with \(N_{T}\) samples. Firstly, we perform offline initial training on the confidence classification network based on the training sample set \(\Phi_{Train}\). Assuming \(\Phi_{O}=\{O_{1},\cdots,O_{N}\}\) is a dataset of \(N\) radar plots to be classified, after extracting features from all these radar plots, the following process is performed as follows:  
Step 1: The trained confidence network classifier is used to obtain the class membership of these samples. The class membership of each sample \(O_{i}\) can be expressed as \(\mu_{i}=(\mu_{i1},\cdots,\mu_{ic})\), satisfying the following equation. Since the classifier's outputs only contain three classes--target, clutter, and uncertainty--the value of \(C\) is 3. \[\sum_{j=1}^{c}\mu_{ij}=1,\mu_{ij}\in[0,1] \tag{29}\]  
Step 2: Select \(K\)-nearest neighbor samples based on the intrinsic feature distribution of these radar plots. As shown in Sections 3.1 and 3.2, construct and combine evidence to obtain the global mass function for each sample such that \(\{(m_{i}^{G}),i=1,\cdots,N\}\). Figure 4: The design of confidence classification network for radar plots. Step 3: Calculate the confidence distances between \(\mu_{i}\) and \(m_{i}^{G}\) of each sample using the following equation. \[cd_{i}=\sqrt{\sum_{c_{j}\in\{\omega_{T},\omega_{c},\Theta\}}(\mu_{i}(C_{j})-m_{i} ^{G}(C_{j}))^{2}} \tag{30}\]  
Step 4: When \(cd_{i}\) is less than a certain threshold represented by \(T_{cd}\), it indicates that the classifier has good consistency with the data distribution results. These samples can be divided into two subsets which are defined as follows:  
\[\Phi_{1}=\{O_{i}:\max\limits_{c_{j}\in\{\omega_{T},\omega_{c},\Theta\}}\
eq m _{i}^{G}(\Theta)\} \tag{31}\]  
\[\Phi_{2}=\{O_{i}:\max\limits_{c_{j}\in\{\omega_{T},\omega_{c},\Theta\}}=m_{i} ^{G}(\Theta)\} \tag{32}\]  
The classification results of the samples in subset \(\Phi_{1}\) can be direct output, which gives a judgment on whether the sample is target or clutter. At the same time, these samples are also used to provide decision assistance to subset \(\Phi_{2}\). Then, the center of these classes can be obtained as follows. \[m_{C_{q}}=\sum_{C_{q}\subseteq\{\omega_{T},\omega_{c}\},O_{i}\in\Phi_{1}}m_{ i}^{G}(C_{q})/N_{\Phi_{1}} \tag{33}\]  
For the samples \(O_{j}\) in subset \(\Phi_{2}\), we calculate their confidence distances from the class center \(\omega_{T}\) and \(\omega_{c}\) separately and assign these samples to the class with the minimum confidence distance in sequence. \[C_{q}=\operatorname*{agrm{}}_{C_{q}\in\{\omega_{T},\omega_{c}\},O_{i}\in\Phi _{1}}\left\|m_{j}^{G}(C_{q})-m_{C_{q}}\right\| \tag{34}\]  
Step 5: When \(cd_{i}\) is bigger than the threshold \(T_{cd}\), it indicates that the classifier and data distribution have different perceptions of samples' confidence. Then, we need to make the following evidence correction. \[m_{i}^{\prime}(C_{j})=(1-\omega_{m})\mu_{i}(C_{j})+\omega_{m}m_{i}^{G}(C_{j}),c_{j}\in\{\omega_{T},\omega_{c},\Theta\} \tag{35}\]  
Here, \(\omega_{m}\) is the confidence correction factor, which is usually set to 0.5 if there is not much experience to follow. Step 6: These corrected evidences are used as temporary samples to optimize the confidence network classifier. Step 7: For the samples obtained in step 5 that are greater than the confidence threshold, we repeat steps 1 to 6 based on the optimized confidence network classifier. Step 8: A certain number of samples are accurately classified in each iteration update. Therefore, we stop the iterative optimization process until no samples exceed the confidence threshold. Then, all the samples are accurately classified. From the above process, it can be seen that the confidence classification network in the proposed algorithm has two advantages. Firstly, it can quickly provide classification results for easily distinguishable samples, which can also provide decision support for uncertain samples.