# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 4 Experiments
### The Impact of Training Sample Sets  
As is well-known, the performance of recognition algorithms is usually closely related to the training samples. Therefore, the impact of the training samples on each algorithm is mainly analyzed in this section. The dataset used here is a mixture of high-clutter density and low-clutter density samples. It has a total of 4228 radar plots, including 1321 target plots, 2653 clutter plots, and 344 uncertainty plots. Four different scenarios are set here, represented by S1, S2, S3, and S4. In each scenario, a certain number of samples are selected for classifier model training, and the remaining samples are used to test the performance of each algorithm. The specific number of training and testing samples for S1 to S4 is set in Table 5. Here, the recognition accuracy and CPU time are also used as evaluation indicators. The test results of each algorithm are summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental**} & \multicolumn{3}{c}{**Training Samples/Testing Samples**} \\ \cline{2-4}  & **Target** & **Clutter** & **Uncertain** \\ \hline S1 & 200/1121 & 500/2036 & 100/244 \\ S2 & 300/1021 & 1000/1563 & 150/194 \\ S3 & 500/821 & 1500/1063 & 200/144 \\ S4 & 900/421 & 2000/563 & 250/94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The training and sample testing settings in different experimental scenarios.  
Table 6 shows that PSO-MLP, FNN, CNN, and RNN are significantly affected by the number of samples. For example, in the S4 scenario with sufficient samples, the recognition rate of each algorithm can be about 6 to 9 percent higher than S1 with insufficient samples. At the same time, the CPU time of these algorithms is also reduced, as the number of test samples is also decreasing. The recognition accuracy of PSO-SVM does not significantly improve with an increase in the number of training samples from S1 to S4, which are generally only around 0.1 to 0.2. It can be seen that PSO-SVM is a good choice in small sample datasets. The recognition accuracy of the RPREC is also not significantly affected by the number of samples but at the cost of sacrificing a significant amount of time to optimize the classifier. So, it can be seen that the time for S4 is 19.31, but S1 requires 42.55, which is almost three times higher.  
In addition, compared to other algorithms, the proposed algorithm can basically maintain the best recognition accuracy. Of course, it inevitably has the most CPU time in each scenario. This is because the classifier in other algorithms only needs to be trained offline and can be directly applied during testing. However, the RPREC needs to continuously analyze radar plots during the testing process to optimize the classifier. Specifically, the network model classifier of the proposed algorithm needs to be trained by certain samples in advance. Then, the self-learning of the classifier can be achieved through iterative updates of the class confidence of the target data, ultimately gradually improving the classification accuracy. This is why the fewer training samples, the longer it takes for the classifier to optimize to good performance. Therefore, how to maintain good timeliness like other algorithms is also the focus of future research.