# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 2 Related Work
\[f_{SDIF}(\tau)=k\times(E-C)e^{\frac{\tau}{kN}} \tag{2}\]  
where \(\alpha\in[0,1]\) is an adjustable parameter, \(N\) is the number of histogram intervals, \(C\) is the histogram order, \(E\) is the number of pulses, and \(k\) is a constant set base on experience. In [15], the SDIF deinterleaving was improved, through the clustering algorithm to deal with the difference of random distribution, and it is combined with the PRI transformation  
Figure 1: Radar pulse signals deinterleaving. after the potential PRI exceeds the threshold, the harmonics can be further suppressed, and the estimated PRI accuracy can be improved. When the aliasing pulse is 16% and the missing pulse is 20%, the deinterleaving is complete, but the accuracy is not high. In [16], the overlapped PRI bins are introduced to sort different PRI modulation modes, and the phase factor is introduced into the autocorrelation function, which can effectively suppress harmonics and resist noise. In Equation (3), the PRI transformation first regards the radar pulse sequence as the sum of a series of unit impulse responses, where \(N\) is the number of pulse sequences. Then, Equation (4) is obtained by the autocorrelation function of Equation (3), and the pulse sequence is used in PRI transformation to obtain Equation (5). where \(\tau>0\), and \(j\) is the imaginary number unit. Substitute Equation (3) into Equation (5) to obtain Equation (6). \[g(t)=\sum_{n=0}^{N-1}\delta(t-t_{n}) \tag{3}\]  
\[G(\tau)=\int_{-\infty}^{\infty}g(t)g(t+\tau)d\tau \tag{4}\]  
\[D(\tau)=\int_{-\infty}^{\infty}g(t)g(t+\tau)e^{\frac{2\pi\mu}{\tau}}d\tau \tag{5}\]  
\[D(\tau)=\sum_{n=1}^{N-1}\sum_{m=0}^{n-1}\delta(\tau-t_{n}+t_{m})e^{[\frac{2 \pi\mu}{\tau-t_{m}}]} \tag{6}\]  
where \(|D(\tau)|\) is the spectral function of the pulsed PRI. The peak value in this spectral function is the estimated potential PRI. \(e^{\frac{2\pi\mu}{\tau}}\) is the introduced phase factor, which can suppress the harmonics generated by PRI transformation and estimate the value of PRI accurately. When estimating potential PRI, the pulse current density, sub-harmonic elimination, and noise elimination rules are considered to adapt to the pulse signal [17]. The traditional PRI transformation cannot effectively deinterleave the staggered PRI, which has limitations. In [18], through the improved machine learning clustering method, combined with PDW parameters such as pulse width and radio frequency, the pre-sorting is completed, which can help the main sorting to dilute the pulse stream. However, for example, K-means is difficult to estimate the number of clusters K in advance, and the cluster radius of DBSCAN also requires strong prior knowledge of the intercepted signals, which is not practical. In recent years, some scholars have also used deep learning methods to complete signal deinterleaving. In [19; 20], the time-frequency analysis method and convolutional neural network are used for recognition of the signal's intra-pulse modulation characteristics and complete signal deinterleaving task in a specific situation, opening up a new research direction. In [21], the received pulse stream is first sorted by the traditional histogram method, and then, the processed pulse sequence is fed to long short-term memory (LSTM) for training. Through the learning of the existing sequence, a model that can predict the same distribution of pulses is trained to predict the next arriving pulse. This method has a high dependence on the traditional deinterleaving algorithm. Ref. [22] combines a recurrent neural network and attention module to complete the identification of different PRI modulation modes, but did not complete the deinterleaving task. Deep learning has many advantages, and it has a good performance in object detection, recognition, and semantic segmentation. Using deep learning to complete the deinterleaving of radar emitter signals and extracting signal features through neural networks can improve the anti-noise ability and reduce manual participation in the signal deinterleaving.