# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 4 Experiment
### Pattern Recognition Model Setup  
We performed data preprocessing, model training, and hyperparameter optimization for the pattern recognition model, to process the PDW train output from the deinterleaving model. For the preprocessing step, we structured the frequency information and we calculated the PRI information from each PDW train into a multivariate time series data vector, \(x\in R^{2\times,samples}\). We applied a modified minmax normalization, to accommodate different scales of frequencies and PRI values, as follows:  
\[x_{\text{normalized}}=\frac{x-\min(x)+\alpha}{\max(x)-\min(x)+2\alpha},\]  
where  
\[\alpha=\frac{1000}{\text{std}(x)}.\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Search Space** \\ \hline \(AA\)\({}_{\text{itres}}\) (\({}^{\circ}\)) & [1, 5] \\ \(Freq_{\text{itres}}\) (kHz) & [100,000, 1,000,000] \\ \(PW_{\text{itres}}\) (ms) & [1, 100] \\ \(PA_{\text{itres}}\) (db) & [1, 10] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Search space for Bayesian optimization for the deinterleaving model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AOA (\({}^{\circ}\))** & **Frequency (kHz)** & **PW (ms)** & **PA (db)** & **Score** \\ \hline
3.7454 & 4,756,035.8167 & 2993.2924 & 14.9079 & 81.8952 \\
7.7518 & 4,416,538.1391 & 1862.1235 & 5.3162 & 92.3387 \\
8.8703 & 2,678,465.3465 & 2655.9410 & 5.8810 & 93.7143 \\
1.9605 & 1,071,099.6430 & 3491.7165 & 1.1764 & 95.4718 \\
6.0112 & 3,554,959.2601 & 4849.5493 & 1.3911 & 95.4718 \\
3.8239 & 1,135,701.5285 & 4002.3206 & 5.5540 & 95.4718 \\
6.9129 & 1,212,423.3194 & 3500.1928 & 14.2985 & 95.4718 \\
5.1407 & 1,211,903.6938 & 2839.4390 & 15.1432 & 95.4718 \\
2.6256 & 1,124,927.1818 & 4329.3377 & 11.2010 & 95.4718 \\
7.6594 & 1,082,501.6882 & 2062.7617 & 1.8047 & 95.4718 \\
1.5602 & 822,172.8757 & 4330.8807 & 2.1036 & 95.4983 \\
7.1875 & 1,101,082.8165 & 928.2235 & 11.0920 & 105.7289 \\
7.4260 & 2,649,166.9935 & 98.3829 & 6.7065 & 105.7543 \\
0.0630 & 2,323,468.2073 & 2633.7424 & 19.5565 & 105.7915 \\
8.9805 & 1,065,441.2204 & 182.7483 & 4.6231 & 105.8310 \\
8.3244 & 1,101,078.5979 & 917.0225 & 4.4547 & 106.8509 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bayesian optimization result for deinterleaving model. We here added \(\alpha\) to the min-max normalization process, to effectively deal with the case of fixed frequency patterns and stable PRI patterns. As shown in Figure 6, without the \(\alpha\), even slight noise could be amplified, making it difficult to discern fixed or stable patterns. The preprocessed data was divided into train and validation sets, in a ratio of 9:1. The reason why we divided the dataset into training and validation sets was to assess the model's generalization performance, ensuring it did not overfit to the train set. The train set was used to train the radar pattern recognition model. We adopted different types of RNN encoder, such as vanilla RNN, LSTM, and GRU, while keeping all the other conditions fixed. Initially, we set the dimension of the hidden state in the RNN encoder to 8. The final context vector obtained from the RNN encoder with four layers was of dimension 4 \(\times\) 8 = 32. The number of three fully connected layers nodes that made up the classifier was set to 1024, 512, and 256 in that order. The model architecture is presented in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Layer Type** & **Input–Output Shape** \\ \hline RNN encoder & 4-layered RNN encoder & \(2\times samples\to 32\) \\ \hline \multirow{4}{*}{Classifier} & Fully connected \\  & Batch normalization \\  & ReLU \\ \cline{1-1}  & Fully connected \\ \cline{1-1}  & Batch normalization \\ \cline{1-1}  & ReLU \\ \cline{1-1}  & Fully connected \\ \cline{1-1}  & Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The overall model architecture. Figure 6: The original minmax normalization and modified minmax normalization were compared with frequency pattern examples. Signals with patterns other than the fixed pattern showed minimal differences between their results (below).