# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Algorithm Performance Evaluation
The numbers of radar plots in each class are shown in Table 10. 30 Monte Carlo experiments were conducted, with half of the radar plot selected for classifier training each time, and the remaining plot used to test the algorithm performance. These indicators include the classification accuracy, target loss rate, clutter removal rate and CPU time. The experiment also recorded the correct and incorrect numbers of target plots obtained by each algorithm during the classification process, represented by \(T^{Y}\) and \(T^{N}\), respectively, as well as the correct and incorrect numbers of clutter, represented by \(C^{Y}\) and \(C^{N}\), respectively. The experimental results for each indicator are shown in Table 11.

The statistical results recorded in Table 11 show that the proposed RPC-EAU algorithm performs the best compared to other methods. Although many clutter plots can have a certain impact on classification, RPC-EAU can still maintain a classification accuracy of 0.95. In contrast, the classification accuracy and clutter removal rate of PSO-SVM and IKNN remain around 0.85, with a target loss rate of 0.1. The performance of RPC-FNN and RPC-RNN has improved, with a classification accuracy of around 0.9 for both targets \begin{table}
\begin{tabular}{c c c c c c} \hline
**The Property Parameter** & \multicolumn{4}{c}{**The Experimental Statistical Results of RPC-EAU**} \\ \hline Evidence update times & 100 & 200 & 300 & 400 & 500 \\ \hline CPU time (s) & 0.75 & 1.44 & 2.15 & 2.85 & 3.57 \\ \hline \end{tabular}
\end{table} 
Table 9: The specific relationship between the CPU time and evidence update times. and clutters, and the target loss rate is also less than 0.7. The classification accuracy of RPEC can continue to improve by 4 percentage points, but it takes too much time to reach 33.79 s. RPC-EAU has the highest classification accuracy for targets and clutter, also the lowest target loss rate. At present, the relative disadvantage of RPC-EAU is that the CPU time cannot be as short as PSO-SVM. The reason is that the classification accuracy and computational time are usually mutual restrained, which means that when pursuing high radar plot classification accuracy, paying a certain cost of computational time is inevitable. Fortunately, the CPU time of RPC-EAU can be maintained at the same level as that of RPC-FNN and RPC-RNN, rather than being as huge as RPREC. This is crucial and meaningful in specific applications. ## 5 Conclusions

In this study, a radar plot classification algorithm based on evidence adaptive updating called RPC-EAU is proposed. It first constructs ten dimensional features of radar plots for classification. Then, the construction and combination of mass functions based on feature samples distribution are designed. Finally, the design and iterative update strategy of the confidence network classifier are presented. Several experiments based on synthetic and real radar plots are given at the end of the paper. The statistical results from these experiments show that compared with some classic radar plot recognition algorithms, the proposed RPC-EAU algorithm can improve the classification accuracy by 1 to 10 percentage points, reaching around 0.95. The target loss rate of RPC-EAU is also the lowest, around 0.02. The CPU time of RPC-EAU is about 2 to 4 s, which can maintain the same level as RPC-FNN and RPC-RNN. Therefore, RPC-EAU performs the best in the comprehensive evaluation of all indicators. In addition, RPC-EAU fully utilizes the collaborative decision-making strategy based on classifier and data distribution, which quickly outputs mutually assured results and comprehensively optimizes uncertain data. This not only improves classification accuracy, but also reduces the number of iterations to achieve low computational time. Therefore, it has significant advantages in the classification of uncertain data and has little dependence on samples.