# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 4 Experimental Results and Discussion
### Experimental Dataset  
The software of generating the experimental data is Python3.7. Pytorch1.5 is used to train and predict the model, and GPU is RTX3090. We summarize the experimental data on the specific parameters of 15 radar emitters in Table 1.  
The experimental data include 15 radar emitter signals, and the intrapulse modulation methods include normal signals, linear frequency modulation signals, nonlinear frequency modulation signals, etc. It includes different pulse widths and radio frequency ranges, different interference rates of aliasing pulses and missing pulses, and measurement errors. The missing pulse is a random miss that obeys the uniform distribution, and the aliasing pulse is a uniform random distribution from the initial TOA to the end TOA. The TOA measurement error conforms to normal distribution with an average value of 0 and a standard deviation value of \(\sigma=2.5\)\(\upmu\)s. If the maximum value of the TOA measurement error exceeds twice \(\sigma\), it is set equal to \(2\sigma\).  
The modulation types of PRI are constant PRI, staggered PRI, and jittered PRI. The working parameters of PRI are shown in Table 2. The value of PRI is \([100,\ 500]\) and obeys the uniform distribution. The jitter error of the constant PRI does not exceed \(1\%\). The number of sub-periods of the staggered PRI is set as \(N_{i}=3\) or \(N_{i}=4\). The jitter range of the jittered PRI \(\delta\in[5\%,25\%]\), where the jitter rate \(\delta\) conforms to normal distribution with an average value of 0.15 and a standard deviation value of \(\sigma=0.05\), if \(\delta<0.05\), then \(\delta=0.05\), and if \(\delta>0.25\), then \(\delta=0.25\).  
The dataset is the 15 radar emitter signals in Table 1. Randomly select 2-4 radar pulses and match them with different PRI modulation modes and working parameters in Table 2 to generate a total of 2000 aliased pulse streams. Use 80% of the experim  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Radar** & **Intrapulse** & **PRI** & **Radio** & **Pulse Width** & **Pulse** & **Pulse** & **TOA** \\
**Number** & **Modulation** & **Modulation** & **Frequency** & **(\(\upmu\)s)** & **Missing** & **Aliasing** & **Measurement** \\  & & **Mode** & **(GHz)** & **Rate** & **Rate** & **Error (\(\upmu\)s)** \\ \hline
1 & PSK & Constant & 1.213–1.472 & 0.42–0.45 & 10\% & 10\% & \(\pm 5\) \\
2 & LFM & Staggered & 2.524–2.763 & 1.12–1.18 & 20\% & 10\% & \(\pm 5\) \\
3 & LFM & Constant & 2.427–2.663 & 1.16–1.21 & 15\% & 15\% & \(\pm 5\) \\
4 & NLFM & Constant & 0.726–0.919 & 0.56–0.62 & 20\% & 10\% & \(\pm 5\) \\
5 & PSK & Jittered & 1.857–2.137 & 0.49–0.53 & 5\% & 5\% & \(\pm 5\) \\
6 & PSK & Jittered & 2.618–2.903 & 0.47–0.51 & 10\% & 5\% & \(\pm 5\) \\
7 & FSK & Staggered & 0.625–0.745 & 0.70–0.75 & 15\% & 15\% & \(\pm 5\) \\
8 & NS & Staggered & 2.175–2.352 & 0.88–0.93 & 20\% & 5\% & \(\pm 5\) \\
9 & LFM & Constant & 1.211–1.477 & 0.92–0.97 & 20\% & 20\% & \(\pm 5\) \\
10 & LFM & Constant & 1.154–1.338 & 0.33–0.39 & 20\% & 15\% & \(\pm 5\) \\
11 & FSK & Constant & 0.972–1.315 & 1.12–1.18 & 20\% & 5\% & \(\pm 5\) \\
12 & NLFM & Jittered & 1.201–1.678 & 0.52–0.58 & 10\% & 20\% & \(\pm 5\) \\
13 & NS & Staggered & 2.235–2.516 & 0.81–0.85 & 15\% & 20\% & \(\pm 5\) \\
14 & LFM & Jittered & 1.783–2.154 & 0.63–0.69 & 15\% & 15\% & \(\pm 5\) \\
15 & LFM & Constant & 2.312–2.613 & 0.40–0.49 & 5\% & 10\% & \(\pm 5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fifteen radar emitter signal working parameter.  
Figure 9: Radar pulse stream in presence of aliasing pulse and missing pulse.  
10% for validation, and 10% for testing. Evaluation indicators are divided into pixel accuracy, which evaluates the fitting ability of the semantic segmentation network. Another evaluation indicator is F1-Score, which represents the ability of signal deinterleaving.