# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 1 Introduction  
Radar is an important tool for remote detection and tracking of targets, with significant applications in both military and civil fields. Radar emits electromagnetic wave energy into a certain direction in space and receives echoes reflected by objects in that direction, thereby discovering and determining targets. In the actual detection of radar, in addition to the real target echo, the received echo also contains much cloud and rain clutter, sea clutter, and ground clutter. Cloud and rain clutter refers to the abnormal clutter caused by thereflection of radar beams by clouds and rain. Sea clutter refers to the backscattered echoes of the sea surface under radar illumination. Ground clutter refers to ground echoes caused by the super-refraction of radar beams. The characteristics of these mixed clutters are complex and irregular, which will increase the difficulty of identifying real targets. Under the processing of existing target detection methods, clutter cannot be completely eliminated. Therefore, determining whether a target is true or false in a cluttered background is also a basic task in radar data processing [1; 2; 3]. These large numbers of clutter plots will have two impacts on subsequent radar data processing. First, they can generate a large number of false trajectories, making it difficult to accurately track real targets. In addition, they will inevitably waste computing resources and affect the processing efficiency of the system. Therefore, it is necessary to further classify the target and clutter after object detection and to filter out a large number of false clutter plots that affect radar data processing. In order to effectively achieve accurate classification of targets and clutter plots, some scholars have conducted a series of studies [4; 5; 6; 7]. In [5; 7], the radar plot classification methods based on an optimized support vector machine were proposed, which achieved the distinction between targets and clutter by extracting echo features and fusing multiple features. In [4], [PERSON] et al. used a weighted KNN model to identify the authenticity of radar plots. In [6], [PERSON] et al. proposed a false plot identification method based on multi-frame clustering. These methods are limited to certain scenarios, and the recognition results are not good enough. In recent years, the development and application of artificial intelligence algorithms, especially deep learning network models, have provided a lot of help in solving problems. So, in the field of radar point data processing, fully connected neural networks [8; 9], convolutional neural networks [10; 11; 12], recurrent neural networks [13], multi-layer perceptron [14; 15], and their variations or combinations with other algorithms [16; 17], have been widely studied. Due to their better feature extraction ability, they can improve the classification accuracy to a certain extent, reaching over 90%. However, due to the lack of representation of uncertain information, they can only make hard judgments of yes or no. But in many specific applications, multi-step decision-making is required. Initially, uncertain classifications can be retained, and then accurate classification can be gradually achieved based on supplementary evidence information. In order to effectively represent and process uncertain and imprecise information, the belief function theory has been proposed and developed [18; 19; 20]. At first, it was mainly used to achieve the fusion of multi-sensor data, but later it was expanded to many applications by some scholars. These applications mainly include pattern classification [21; 22; 23; 24; 25; 26], evidential clustering [27; 28; 29; 30; 31; 32], multi-sensor data fusion [33], decision making [34; 35], image segmentation [36; 37], security assessment [38; 39], abnormal condition detection [40], and user preference analysis [41; 42]. The confidence function has been fully proven to be effective in many fields.