# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 5 Conclusions  
In this paper, we presented a novel batch streaming radar emitter classification pipeline. The proposed pipeline addresses the challenges of radar deinterleaving and radar pattern recognition, both of which are essential components of radar emitter classification. By designing machine learning and deep learning models, we achieved 100% deinterleaving accuracy and up to 98% pattern recognition accuracy on the test dataset in our pipeline.  
We propose three directions for future work. First, we can improve the pipeline, so as to handle noisy data by adding a noise filter appropriate for radar data at the front of the pipeline. Second, the hyperparameter optimization technique can be improved with a variety of tuning scheduler, which can produce faster and better hyperparameter optimization results. Lastly, we can validate the generalization performance of our model by deploying the proposed pipeline on a real military weapon system and processing real radar data.  
Conceptualization, D.H.P., D.-H.S., J.-H.B. and W.-J.L.; methodology, D.H.P.; software, D.H.P.; validation, D.H.P., D.-H.S., J.-H.B. and W.-J.L.; formal analysis, D.H.P.; investigation, D.H.P., D.-H.S., J.-H.B. and W.-J.L.; resources, D.H.P., D.-H.S., J.-H.B. and W.-J.L.; data curation, D.H.P., D.-H.S., J.-H.B. and W.-J.L.; writing--original draft preparation, D.H.P.; writing--review and editing, D.E.C.; visualization, D.H.P.; supervision, D.E.C.; project administration, D.E.C.; funding acquisition, D.E.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**context\_vec\_dim** & **n\_layers** & **fc\_1** & **fc\_2** & **fc\_3** & **lr** & **Score** \\ \hline
2 & 1 & 1024 & 433 & 1024 & \(2.910\times 10^{-5}\) & 15.7387 \\
2 & 1 & 1024 & 432 & 1024 & \(4.900\times 10^{-5}\) & 17.8266 \\
15 & 4 & 359 & 487 & 545 & \(1.017\times 10^{-5}\) & 77.2090 \\
6 & 6 & 376 & 743 & 936 & \(3.707\times 10^{-5}\) & 77.7451 \\
13 & 6 & 716 & 376 & 376 & \(4.778\times 10^{-5}\) & 78.4101 \\
9 & 4 & 820 & 294 & 999 & \(7.085\times 10^{-6}\) & 79.7196 \\
15 & 2 & 257 & 1022 & 665 & \(1.226\times 10^{-5}\) & 82.7147 \\
15 & 5 & 363 & 480 & 537 & \(1.811\times 10^{-5}\) & 86.2460 \\
27 & 2 & 398 & 490 & 659 & \(1.455\times 10^{-5}\) & 86.3491 \\
18 & 1 & 724 & 367 & 378 & \(1.711\times 10^{-5}\) & 87.4884 \\
3 & 5 & 789 & 269 & 997 & \(4.641\times 10^{-5}\) & 89.5969 \\
3 & 6 & 807 & 281 & 1000 & \(2.615\times 10^{-5}\) & 89.9010 \\
12 & 1 & 815 & 294 & 995 & \(4.367\times 10^{-5}\) & 91.1434 \\
4 & 5 & 800 & 272 & 1001 & \(4.398\times 10^{-5}\) & 98.0823 \\
5 & 7 & 667 & 256 & 980 & \(4.639\times 10^{-5}\) & 98.2472 \\
8 & 6 & 372 & 745 & 942 & \(2.506\times 10^{-5}\) & 98.9587 \\ \hline \end{tabular}
\end{table}
Table 8: Bayesian optimization result for pattern recognition model (GRU).  
\begin{table}
\begin{tabular}{c c c} \hline
**RNN Encoder Type** & **LSTM** & **GRU** \\ \hline Radar emitter classification pipeline accuracy & 98.715\% & 98.717\% \\ \hline \end{tabular}
\end{table}
Table 9: Radar emitter classification pipeline test for test data.  
**Funding:** This research was performed based on cooperation with KAIST-LIG Nex1 Cooperation (No. G01220631).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to legal restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.