# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 4 Experiments
### Radar Plot Recognition  
This experiment is based on two types of radar-measured datasets, high-density clutter, and low-density clutter, which have been introduced at the beginning of Section 4. In each scenario, half of the radar plot data were randomly selected as training samples, and the remaining were used as testing samples. In order to effectively verify the performance of the RPREC, we compared it with some classic radar dot recognition algorithms, including PSO-SVM [3], PSO-MLP [54], FNN [53], CNN [55], and RNN [56]. Here, the percentage of recognition accuracy and CPU time are used as important indicators to measure the performance of these algorithms. The experimental results based on the high-density clutter dataset are shown in Table 3, and the experimental results based on the low-density clutter dataset are shown in Table 4. In these tables, \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) represent, respectively, target, clutter, and uncertainty. The experimental results shown in Table 3 indicate that PSO-SVM, PSO-MLP, FNN, CNN, and RNN have some shortcomings in the representation of uncertain data. Therefore, these algorithms focus on the binary classification of targets and clutter, so the uncertain \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **The Radar** \\ **Plots** \\ \end{tabular} } & \multicolumn{6}{c}{**Recognition Accuracy/CPU Time (s)**} \\ \cline{2-7}  & **PSO-SVM** & **PSO-MLP** & **FNN** & **CNN** & **RNN** & **RPREC** \\ \hline \(\omega_{1}\) & 0.852/0.12 & 0.833/0.43 & 0.821/0.89 & 0.851/1.08 & 0.873/1.32 & 0.923/12.55 \\ \hline \(\omega_{2}\) & 0.856/0.31 & 0.842/0.98 & 0.812/1.09 & 0.863/1.53 & 0.857/2.21 & 0.936/15.39 \\ \hline \(\omega_{3}\) & / & / & / & / & / & 0.925/11.37 \\ \hline \(All\) & 0.854/0.43 & 0.836/1.41 & 0.817/1.98 & 0.857/2.61 & 0.865/3.53 & 0.928/39.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The recognition accuracy and CPU time of six different algorithms on the low-density clutter dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **The Radar** \\ **Plots** \\ \end{tabular} } & \multicolumn{6}{c}{**Recognition Accuracy/CPU Time (s)**} \\ \cline{2-7}  & **PSO-SVM** & **PSO-MLP** & **FNN** & **CNN** & **RNN** & **RPREC** \\ \hline \(\omega_{1}\) & 0.821/0.52 & 0.853/0.73 & 0.861/1.21 & 0.911/2.71 & 0.923/3.55 & 0.921/9.55 \\ \hline \(\omega_{2}\) & 0.837/1.12 & 0.847/1.71 & 0.832/3.26 & 0.913/5.13 & 0.927/6.32 & 0.932/18.35 \\ \hline \(\omega_{3}\) & / & / & / & / & / & 0.945/3.37 \\ \hline \(All\) & 0.829/1.64 & 0.850/2.44 & 0.847/4.47 & 0.902/7.84 & 0.925/9.87 & 0.932/31.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The recognition accuracy and CPU time of six different algorithms on the high-density clutter dataset. Figure 7: The distribution of the dataset with low-clutter density. data represented by \(\omega_{3}\) will be hard to classify into targets or clutter. This will affect their recognition accuracy. The recognition accuracy of PSO-SVM is about 0.83, and the CPU time is 1.64. The recognition accuracy of PSO-MLP and the FNN is similar, but the FNN takes more time. Compared to the FNN, the RNN and CNN have advantages in deep feature extraction of radar plots, resulting in a higher recognition accuracy of over 0.91. Of course, the CPU time has also doubled accordingly. The most significant feature of the RPREC proposed in this article is its ability to characterize and measure uncertain data. Therefore, it can be seen from the fifth row in Table 3 that uncertain plots have been effectively identified with a recognition accuracy of 0.945. In addition, the RPREC also maintains good performance in target and clutter recognition, with recognition accuracy rates of 0.921 and 0.932, respectively. However, the RPREC also has a significant limitation in that it has a large CPU time, which is 3 to 15 times that of other algorithms.