# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Algorithm Performance Evaluation
RPC-RNN adopts the complex network structure of 100 hidden layers as described in the reference. In the RPREC, the number of iterations is set to 1000, and the confidence threshold parameters \(T1\) and \(T2\) are selected as 0.4 and 0.1, respectively. In the proposed algorithm RPC-EAU, the parameter \(K\) representing the number of nearest neighbor decision evidences is set to 8, and the confidence threshold parameter \(T_{cd}\) is set to 0.2. In order to eliminate the impact of sample selection, we conducted three independent experiments, randomly selecting half of the samples for training and testing the remaining ones each time. The specific situation of the samples selected in these three experiments is shown in Table 6. The classification accuracy, target loss rate and clutter removal rate that are designed in Section 3.5 are used to evaluate the effectiveness of the algorithms. At the same time, in order to verify the timeliness of these algorithms, the CPU time was also recorded in the experiment. The average results of three experiments on these indicators are recorded in Table 7. The \(CA\), \(TLR\), \(CRR\) and \(CT\) are, respectively, used to represent the classification accuracy, target loss rate, clutter removal rate and CPU time. Figure 6: The distribution of the experimental dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Class Label**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & \(\omega_{T}\) & \(\omega_{c}\) & \(\Theta\) & \\ \hline Number & 372 & 263 & 86 & 721 \\ \hline \end{tabular}
\end{table} 
Table 5: The number of samples in each class. The experimental results in Table 7 show that the radar plot classification algorithms PSO-SVM and IKNN based on traditional machine learning have the shortest CPU time, both within 1 s. However, their classification accuracy and clutter removal rates are also the lowest, generally in the range of 0.83 to 0.87, with a target loss rate of about 0.12. Compared to PSO-SVM and IKNN, the algorithms RPC-FNN and RPC-FNN based on deep learning improved the classification accuracy and clutter removal rate, reaching around 0.91. At the same time, the target loss rate decreased to below 0.06, but the CPU time also increased by two to five times. RPREC has the advantages of deep learning and evidence clustering. Compared to RPC-FNN and RPC-RNN, the classification accuracy and clutter removal rate can be improved by about 3 percentage points. The target loss rate has also been reduced by more than half. But the cost is a huge CPU time, increased by more than 10 times to 24.21 s. The proposed algorithm RPC-EAU has a similar target loss rate compared to RPREC, with slightly improved classification accuracy and clutter removal rate. But the CPU time has significantly reduced to 2.87 s; although not as fast as PSO-SVM and IKNN, it can still maintain the same level of CPU time as RPC-FNN and RPC-RNN. Compared with other radar plot recognition algorithms in this experiment, RPC-EAU has the highest classification accuracy and clutter removal rate, as well as the lowest target loss rate. The CPU time of RPC-EAU is also moderate. Therefore, it can be seen that traditional radar plot classification algorithms based on machine learning are the most efficient in terms of computational speed, but their drawback is that the distinction between targets and clutters is not accurate enough. The radar plot classification algorithm based on deep learning can extract more refined features through complex networks, thereby obtaining better discrimination between targets and clutters. RPREC can fully utilize the distribution characteristics of online radar plots and iteratively optimize the network classifier to achieve high classification accuracy. However, each round of clustering analysis and classifier retraining will generate a lot of computational time.