# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 1 Introduction
Considering that identifying targets and clutter in radar plots is essentially a data classification problem, combining belief functions with deep learning networks to improve classification accuracy is a meaningful topic. Therefore, we proposed the radar plot recognition algorithm based on adaptive evidence classification (RPREC) [43; 44], which effectively improves the classification accuracy of radar plots. The limitation of RPREC is that the classifier needs to be retrained during each iteration process. This consumes a lot of computing time and is not conducive to the applications that pursue timeliness. But the iterative updates adopted by RPREC provide us with a good idea for researching the new algorithms in this paper. Therefore, in order to further improve the classification accuracy, and also to effectively shorten the calculation time, the radar plot classification algorithm based on evidence adaptive updating (RPC-EAU) is proposed in this paper. It mainly consists of three parts. Firstly, we establish multi-dimensional features of radar plots that can be used to distinguish between targets and clutter. Secondly, based on the distribution of samples in the feature space, the mass function was constructed, optimized, and combined. Then, we designed a confidence network classifier based on deep learning, which can give confidence that any radar plot belongs to the target, clutter, and uncertain classes. Finally, an iterative update strategy for the evidences and confidence classifier was established. When there is no significant difference in the judgment between the classifier and the combined evidence, the classification result is directly output. When there are certain differences, the evidence is corrected and the classifier is optimized. The optimized classifier then continues to classify radar plots that are difficult to distinguish. It can be seen that RPC-EAU fully utilizes a collaborative decision-making strategy based on classifiers and data distribution, which not only ensures efficient output of easily classified radar plots, but also ensures multi-step optimization decision making for difficult-to-classify plots. So, while improving classification accuracy, it is also possible to minimize computation time and ensure the efficiency performance of the model. Several experiments based on synthetic and real radar plots show that proposed RPC-EAU algorithm performs better than some typical radar plot classification methods. RPC-EAU not only improves classification accuracy, but also reduces the number of iterations to achieve low computation time. The statistical results show that it can achieve a classification accuracy of around 0.95 while maintaining a target loss rate below 0.02. The CPU time is about 2 to 4 s, which is at the same level as deep network recognition algorithms. Although these studies are exciting and interesting, there are still some studies worth further investigation, such as the adaptive setting of some parameters in algorithms. The rest of this paper is organized as follows. In Section 2, we will recall the belief functions, evidence classification and the intelligent radar plot classification, respectively. In Section 3, we will introduce the proposed radar plot classification algorithm based on evidence adaptive updating. Finally, several experiments based on synthetic and real radar plots will be presented in Section 4, and the paper will be concluded in Section 5.