# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 1 Introduction  
Due to the influence of detection accuracy and clutter, radars will form a large number of clutter plots when receiving target echoes for processing, which is not conducive to target recognition. Especially in areas with high-clutter density, the target plots can hardly be seen [1; 2]. In order to eliminate radar detection errors, multiple radars will work together. But at this point, they can also cause clutter plots to overlap with each other, forming a dense clutter area with irregular spatial distribution, seriously affecting the recognition accuracy and real-time performance of radar data processing. It can be seen that in radar plot processing, how to effectively detect targets from a large amount of clutter and uncertain plots is the key to achieving precise target tracking by radar [3; 4; 5]. In order to ensure efficient and fast data processing for radar plots, traditional radar plot recognition algorithms usually use binary classification recognition rules to determine whether they are a target or clutter [6; 7; 8; 9]. However, it is well-known that targets cannot be accurately classified in some cases. Therefore, due to the inability to effectively represent the uncertain measurements, this yes or no decision judgment can increase the error rate. This is not conducive to correct decision analysis in these traditional methods. There are two main ways to solve this problem: one is to use belief functions to accurately represent uncertain information, and the other is to use deep neural networks with stronger sample learning ability for classification. The belief functions have significant advantages in dealing with uncertain and imprecise information [10; 11; 12; 13; 14]. So, they have been applied in some fields such as pattern recognition [15; 16], data clustering [17; 18; 19; 20; 21; 22; 23], data classification [24; 25; 26; 27], security assessment [28; 29], sensor information fusion [30], abnormal detection [31], tumor segmentation [32; 33], decision-making [34; 35; 36], community detection [37; 38], and items ofinterest recommendation [39; 40]. Considering the good performance of belief functions in application, evidence classification algorithms have been studied to improve the accuracy of target recognition. Among these methods, the evidence \(K\)-nearest neighbor (EK-NN) is the most representative [25; 41]. Subsequently, in order to adapt to different application scenarios, some modifications of the EK-NN have been studied. For instance, many optimization methods based on the EK-NN were proposed in [42; 43; 44; 45]. Generally, these improved algorithms can have good performances in some domains, such as machine diagnosis [46], process control [47], remote sensing [48], medical image processing [49], and bioinformatics [50; 51], among others. Compared with traditional classification methods, the deep neural network model has strong learning ability and also the advantage of constantly updating with an increase in samples [52; 53; 54; 55; 56]. Therefore, radar plot recognition algorithms based on neural networks have been studied. In reference [53], the full connected neural network (FNN) is used to study the classification of radar clutter and real targets. The authors designed a network with five layers, with nodes in each layer being 8, 64, 128, 32, and 2. When the number of training and testing samples is 6276 and 2000, respectively, the classification accuracy of the FNN can reach approximately 0.83 to 0.88. In reference [54], a multi-layer perceptron algorithm optimized by particle swam optimization was used for radar plot recognition (PSO-MLP), which achieved a good recognition accuracy of 0.857. In reference [55], the convolutional neural network (CNN) was compared with the fully connected neural network (FNN) and support vector machine (SVM). The experimental results show that when the training and testing samples are sufficient, the recognition accuracy of the CNN can reach 0.943.