# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 1 Introduction
In addition, machine learning algorithms that can be considered for application in the radar deinterleaving process include OPTICS (ordering points to identify the clustering structure) [18], HDBSCAN (hierarchical DBSCAN) [19], mean shift [20], and agglomerative [21], etc. In the conventional radar pattern recognition approach, which also relies on hand-crafted features and rule-based methods, similar issues to those encountered in traditional radar deinterleaving are present. To address these issues, recent research has been focusing on the application of deep learning techniques in radar pattern recognition. These methods leverage the power of neural networks to automatically learn relevant features and patterns from PDW trains, allowing for more accurate and robust recognition of radar  
Figure 1: Radar deinterleaving process. patterns. [PERSON] et al. [22] performed radar pattern recognition, using a fully connected layer-based MLP (multi-layer perceptron) classifier. [PERSON] et al. [23] and [PERSON] et al. [24] used a 1D CNN (convolutional neural network) to effectively extract features from radar signals and use them to improve classification performance. [PERSON] et al. [25] demonstrated excellent performance by an attention-based multi-recurrent neural network, which was effective for sequential data processing, compared to existing MLP- and CNN-based radar classification models. Following current research trends, we can consider utilizing recurrent neural networks or attention-mechanism-based transformers [26] for sequential data processing problems like radar pattern recognition. Although extensive research has been conducted on each of these processes individually, to the best of the authors' knowledge a comprehensive pipeline that encompasses both of these processes remains challenging to find. For this paper, we used a modified DBSCAN method for radar deinterleaving and an RNN-based classifier for radar pattern recognition for batch streaming radar data. By combining these two ingredients, we propose a comprehensive radar emitter classification pipeline. While some methods perform better than the DBSCAN algorithm and RNN-based classifiers at each step, the reason why we designed the pipeline based on these two models was to consider the limited hardware resources of real military weapon systems. Even though we adopted machine learning and deep learning models with a simple architecture, it was designed to achieve optimal performance in the proposed pipeline by performing hyperparameter optimization. This paper is structured as follows: In Section 2, we provide background knowledge on DBSCAN, RNN encoder, and Bayesian optimization, which is essential for understanding our proposed radar emitter classification pipeline. In Section 3, we introduce our proposed radar deinterleaving model and radar pattern recognition model, utilizing the background knowledge from Section 2. Moreover, we present our complete radar emitter classification pipeline, which includes both of the components. In Section 4, we set up our proposed models in the pipeline, we experimentally validate the functionality, and we analyze the results. Lastly, in Section 5, we provide a comprehensive conclusion and outline future work.