# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 4 Experiments
### Confidence Threshold Parameter  
In this section, the impact of thresholds \(T1\) and \(T2\) on the recognition performance of the proposed algorithm is analyzed. Here, three performance evaluation indicators, including recognition accuracy, CPU time, and number of loop iterations, were selected in this experiment. For simplicity, the \(Ra\), \(Ct\), and \(Li\) in the table are, respectively, used to represent recognition accuracy, convergence time, and iteration number. The statistical values of these three indicators are based on the average results obtained from 1000 Monte Carlo simulations. The experimental results are shown in Tables 8 and 9 and Figure 9. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment Number** & **Iterations** & **CPU Time (s)** &
\begin{tabular}{c} **Recognition** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{3}{*}{**Exp. 1**} & 200 & 4.35 & 0.339 \\  & 500 & 10.39 & 0.857 \\  & 1000 & 25.83 & 0.951 \\  & 1500 & 35.21 & 0.952 \\ \hline \multirow{3}{*}{**Exp. 2**} & 200 & 3.13 & 0.323 \\  & 500 & 12.27 & 0.876 \\  & 1000 & 23.69 & 0.935 \\  & 1500 & 35.78 & 0.934 \\ \hline \multirow{3}{*}{**Exp. 3**} & 200 & 3.31 & 0.359 \\  & 500 & 9.36 & 0.867 \\ \cline{1-1}  & 1000 & 22.57 & 0.961 \\ \cline{1-1}  & 1500 & 37.31 & 0.962 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The statistical results under different iterations for each experiment. Figure 8: The specific relationship curve between recognition accuracy and iteration number. The red line represents the upper limit of recognition accuracy that the algorithm can approach. As shown in Table 8, when the value of \(T1\) is changed, both \(Ct\) and \(Li\) do not show significant changes. As shown in Figure 9 the recognition accuracy of the algorithm increases first and then decreases as the \(T1\) value increases. When the value of \(T1\) is 0.4, the algorithm's recognition accuracy can reach the maximum value of 0.962 calculated in this experiment. The reason is that when the \(T1\) value is small, the algorithm's ability to eliminate clutter will decrease, leading to an increase in the number of false targets. The high value of \(T1\) can improve the suppression of clutter plots, but it also reduces the algorithm's ability to recognize real targets, which are similar to clutter. Therefore, according to experimental statistical analysis, a \(T1\) value of 0.4 is the best in this scenario. The experimental results in Figure 9 show that as the value of \(T2\) increases, the recognition accuracy of the proposed algorithm gradually decreases. This indicates that minimizing the uncertainty interval of the target's category is more helpful in accurately distinguishing true from false, but it will inevitably affect the convergence speed of the proposed algorithm. Therefore, the experimental results in Table 9 show that as \(T2\) decreases, the values of \(Ct\) and \(Li\) both increase. So, if there is enough time, a value of 0.1 for \(T2\) is the best choice for this scenario. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**T1** & \(Ra\) & **Ct (s)** & \(Li\) \\ \hline
0.1 & 0.942 & 8.73 & 1297 \\
0.2 & 0.947 & 8.91 & 1284 \\
0.3 & 0.959 & 9.15 & 1389 \\
0.4 & 0.962 & 9.36 & 1458 \\
0.5 & 0.938 & 8.62 & 1245 \\
0.6 & 0.941 & 9.02 & 1231 \\
0.7 & 0.931 & 9.51 & 1321 \\
0.8 & 0.921 & 9.32 & 1297 \\
0.9 & 0.913 & 8.79 & 1193 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The recognition results under different values of \(T1\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**T2** & \(Ra\) & **Ct (s)** & \(Li\) \\ \hline
0.1 & 0.959 & 16.92 & 2831 \\
0.2 & 0.956 & 15.30 & 2627 \\
0.3 & 0.935 & 14.97 & 2398 \\
0.4 & 0.941 & 14.92 & 2291 \\
0.5 & 0.937 & 9.36 & 1530 \\
0.6 & 0.919 & 9.16 & 1439 \\
0.7 & 0.922 & 9.28 & 1481 \\
0.8 & 0.918 & 8.75 & 1321 \\
0.9 & 0.912 & 8.12 & 1021 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The recognition results under different values of \(T2\). Figure 9: The relationship curve between recognition accuracy and threshold parameters.