# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 1 Introduction  
Electronic warfare (EW) is a modern form of combat that utilizes various electronic devices, such as electronic sensors, signal jammers, radar systems, and communication networks, as well as electromagnetic waves and telecommunications. It can be broadly divided into three categories: electronic support, electronic attack, and electronic protection [1]. The process of radar emitter classification falls under the domain of electronic support and plays a significant role in analyzing and identifying threats in complex radar signal environments in electronic warfare [2]. When an EW system receives radar signals, it analyzes their features, such as the frequency pattern and pulse repetition interval (PRI) pattern of the signals. These analyzed results are then matched with the threat library of the EW systems, to perform threat identification [3]. However, as electronic countermeasures become sophisticated and new types of complex radar systems are developed and deployed, the classification of these radar emitters has become more challenging [4; 5; 6]. Modern radar emitters often employ a variety of frequency and PRI modulation, which increases the complexity and diversity of radar signals [7]. Consequently, there is a need to develop and implement new methods for classification of these radar emitters. Developing new classification methods could provide a significant advantage in the complex and fast-paced arena of modern electronic warfare. In electronic warfare, classification of the radar emitters involves several steps [8]. First, pulse descriptor words (PDWs) are generated from the received radar signals. Each PDW includes details about a specific radar pulse, such as TOA (time of arrival), AOA (angle of arrival), frequency, PW (pulse width), PA (pulse amplitude), etc. [9]. Second, the generatedPDWs are deinterleaved. Deinterleaving is the process of separating jumbled PDWs back into individual sequences of pulses, which are referred to as PDW trains, each one corresponding to a different emitter [10; 11]. Figure 1 illustrates the radar deinterleaving process. Third, we extract the PRI from each of them, using TOA information, as in [11; 12; 13]:  
\[PRI_{i}=TOA_{i}-TOA_{i-1}.\]  
The PRI is the time interval between consecutive pulses, and is one of the key features that helps us classify the emitter [7; 13]. With this calculated PRI and frequency information, we proceed to the final step, which is the radar pattern recognition process. Each radar emitter has a significant pattern, like a fingerprint, which allows us to recognize it among others [14]. Therefore, by recognizing the frequency pattern and PRI pattern, we can classify the radar emitter of the received signals. The crucial components in the entire radar emitter classification process are radar deinterleaving and radar pattern recognition. These two components have been actively researched and developed, to improve their effectiveness and efficiency. The radar deinterleaving process has traditionally been approached using heuristic-based methods. These methods rely on predefined rules and thresholds to deinterleave the received radar signal. However, these conventional techniques often face limitations in handling complex signal environments and adapting to varying radar signal characteristics [15]. In recent years, machine learning techniques have been gaining significant attention as an effective approach for radar deinterleaving. By utilizing data-driven algorithms, these methods can learn to recognize and separate different radar signals, based on the intricate relationships of radar pulse data. [PERSON] et al. [15] and [PERSON] et al. [16] performed radar deinterleaving by fusing support vector clustering and data field theory, respectively, with the K-means clustering technique. [PERSON] et al. [17] conducted radar deinterleaving using the incremental DBSCAN algorithm, whose performance was experimentally validated by simulated radar data.