# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Algorithm Performance Evaluation  
In RPC-EAU, the classification accuracy, target loss rate and clutter removal rate are used to evaluate its effectiveness. For ease of use, they are represented by \(CA\), \(TLR\), and \(CRR\), respectively. The classification accuracy is the proportion of correct radar plots identified by this algorithm model, defined as follows:  
\[CA=\frac{T^{Y}+C^{Y}}{T^{Y}+T^{N}+C^{Y}+C^{N}} \tag{36}\]  
Here, \(T^{Y}\) and \(C^{Y}\) represent the target and clutter plots that can be correctly classified, while \(T^{N}\) and \(C^{N}\) represent the targets and clutters that have been misclassified as each other, respectively. In practical applications, losing the target plots can sometimes lead to serious impacts, even far higher than the loss of identifying clutter as target. Therefore, it is necessary to consider the target loss rate \(TLR\) as an important evaluation indicator. Its definition formula is as follows:  
\[TLR=\frac{T^{N}}{T^{Y}+T^{N}} \tag{37}\]  
While ensuring that the target plots are not misclassified, it is important to recognize as much clutter as possible for filtering. So, the clutter removal rate is used as another important evaluation indicator, defined as the following formula:  
\[CRR=\frac{C^{Y}}{C^{Y}+C^{N}}\]  
Figure 5: The flowchart for updating class labels of radar plots. ```

**Algorithm 1**RPC-EAU algorithm. ## 4 Experiments

In this section, several experiments based on radar simulation plots and real plots are presented. Firstly, an artificial sample set for these experiment was constructed based on the results of the radar processing program in Section 4.1. In Section 4.2, the effectiveness of the proposed RPC-EAU was evaluated through comparison with some typical radar plot recognition algorithms. Then, the parameter analysis of the RPC-EAU was provided in Section 4.3. Finally, in Section 4.4, the effectiveness of RPC-EAU was also validated in a practical application example. These experiments were conducted on the Lenovo Legion Y9000P, which has an Intel(R) Core(TM) i9-14900 HX CPU @2.5 GHz and 32 GB MEMORY. All these comparison algorithms are implemented based on MATLAB code and executed in MATLAB R2023b under the Windows 11 system. ### Synthetic Dataset

The experimental data come from the X-type sea monitoring and tracking radar. But these data have already been processed by radar tracking programs. So, most of the clutter plots have been filtered out, resulting in the experimental data mainly consisting of three parts: real target trajectory, false trajectory, and some remaining clutter plots. The distribution of the experimental dataset is shown in Figure 6.

Firstly, mark the radar plots corresponding to the real trajectory as targets. Then, mark the radar plots associated with false trajectories as uncertain. Finally, record all remaining plots as clutter. As shown in Table 5, there are 372 targets, 263 clutters, and 86 uncertain samples in the constructed sample set. ### Classification Performance

This experiment is mainly used to verify the classification performance of RPC-EAU, which is based on the sample set constructed in Section 4.1. Meanwhile, we compared various typical radar plot recognition algorithms, which are not only based on traditional machine learning but also on deep learning. These typical radar plot recognition algorithms mainly include PSO-SVM [5], IKNN [4], RPC-FNN [9], RPC-RNN [13] and RPREC [43]. In this experiment, the parameter settings of these algorithms still follow the settings from their respective references. The difference is that the input features of all algorithms are replaced with the 10 dimensional features set in this paper, such that the nearest neighbor parameter of IKNN is still set to 7. In the PSO-SVM, the optimal combination of kernel function and error penalty factor is selected as 15 and 22. The RPC-FNN adopts a five-layer network structure, with nodes in each layer being 10, 64, 128, 32, and 2, respectively.