# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Algorithm Performance Evaluation
This can effectively reduce the misclassification of targets

In Figure 8, it can be intuitively seen that as the value of \(K\) increases, the CPU time also keeps increasing. CPU time is less than 0.5 when \(K\) is less than 3, reaches around 3 when \(K\) is 8, and approaches 6 when \(K\) is 16. The values basically exhibit a linear correspondence relationship. Therefore, based on the above indicators, a value of 8 for \(K\) is the best choice for this experiment. ### Evidence Update Times

In this section, the relationship between the classification performance and evidence update times of the RPC-EAU is mainly analyzed. And the classification accuracy, target loss rate, clutter removal rate and CPU time are still used as the main indicators for evaluating classification performance. The software environment used in this experiment is also the MATLAB R2023b under the Windows 11 system. The specific relationship curves between the classification performance and the evidence adaptive update times of the RPC-EAU are depicted in Figure 9 and Table 9.

Figure 9: The specific relationship curves between the classification performance and evidence update times. As show in Figure 9, the classification accuracy and clutter removal rate of RPC-EAU gradually increase with the increase in the evidence update times. Especially when the evidence update times increases from 1 to 300, they can increase from around 0.1 to 0.9. The target loss rate gradually decreases as the evidence update time increases, from the initial 0.3 to around 0.02. It can be seen that once the classification accuracy, clutter removal rate and target loss rate of RPC-EAU reach a certain level, they will not significantly improve with the increase in evidence update times. As shown in Figure 9, the classification accuracy and the clutter removal rate are always below 0.966, while the target loss rate cannot be lower than 0.02. Table 9 indicates that CPU time is generally proportional to the evidence update times. So, when the evidence update times exceed 400 in this experiment, the classification performance no longer significantly improves. Increasing the number of updates at this point will only waste more CPU time. Therefore, there are some limitations to the performance of the proposed algorithm. Once the classification accuracy, clutter removal rate, and target loss rate of the algorithm reach a certain level, they will not improve again with the increase in the evidence update times. In RPC-EAU, the termination of the evidence adaptive update process is related to the parameters \(K\) and \(T_{cd}\). And the impact of these two parameters on the performance of RPC-EAU was experimentally analyzed in Section 4.3. It can be seen that the adaptability of RPC-EAU is mainly reflected in the ability to autonomously update the evidence and correct the classifier based on the classification results of each round. This can enhance the learning of the intrinsic characteristics of radar plots. At present, the parameters \(K\) and \(T_{cd}\) of RPC-EAU still need to be manually pre-set according to the experimental scenario. This is also the limitation that restricts the promotion and application of the RPC-EAU. ### Application in Real Traffic Control Radar Data

The real radar plots used in this application instance come from the X-band air traffic control radar. The measurement data include 142 true-value annotation data for three targets under multiple scanning cycles, including 52 shots for target 1, 47 shots for target 2, and 43 shots for target 3. Unlike the synthesized samples in Section 4.1, this radar plot has not been processed by the data processing program and contains a rich variety of clutter plots. The number of clutter plots reaches 1531, which is about 10 times more than the number of targets. This will cause significant interference with accurate classification and pose certain challenges for the performance of each algorithm. Still referring to Section 4.2, the specific parameter settings for each algorithm remain the same.