# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 1 Introduction  
In modern warfare, electronic warfare is a very important field [1]. Scientists and technologists from various countries pay great attention to the application and development of electronic technology, hoping to gain control of the space electromagnetic spectrum through the advantages of electronic countermeasures, to win the victory of electronic warfare [2, 3]. The pulse streams deinterleaving of radar signals are the basic part of electronic countermeasures [4]. The definition of deinterleaving is to separate the aliased pulse signals received in space. However, the complex space electromagnetic environment and the application of low-interception radar make it difficult for traditional deinterleaving algorithms to deal with complex situations such as aliasing pulses and missing pulses [5, 6]. For the traditional signal deinterleaving algorithms, there are some disadvantages as follows. First, a lot of prior knowledge is required. For example, in the 1970s and 1980s, the space electromagnetic environment was relatively simple, and the types of radar emitter were fixed, and thus, the template matching method was proposed [7]. There is a need to establish a characteristic database of radar emitter in advance and compare the pulse description words (PDW) of received signals with the database to complete the signal deinterleaving. In [8], a pulse repetition interval (PRI) heuristic search method was proposed. By looking for the difference between the first pulse and the following pulses, it was judged whether the difference between the initial time of arrival (TOA) and the \(i\)-th TOA is in an appropriate interval to complete the deinterleaving. In [9], thepulse end time needed to be estimated in advance before PRI transformation, which is not practical. The traditional histogram method needs to adjust the threshold judgment parameters for different data in order to estimate the potential PRI, which relies on manual experience heavily. However, in most cases, the electronic support measure (ESM) [10] intercepts non-cooperative signals, and it is difficult to estimate the threshold in advance. Some scholars also proposed the method of plane transformation [11], which transforms the pulse signal into a two-dimensional plane for sorting. These methods are seriously disturbed by manual empirical parameters, and the effect on jittered and staggered PRI is not ideal. When aliasing pulses and missing pulses occur, the deinterleaving ability of these methods will be greatly reduced, and they cannot be applied to the current and future complex space electromagnetic environment and emerging new system radars. In this paper, in the case of aliasing pulses and missing pulses, the semantic features of different PRI are extracted by deep learning, the influence of manual experience parameters is reduced, the anti-noise ability of the algorithm is improved, and the deinterleaving of radar signals is completed intelligently. We propose a frequency characteristic matrix (FCM) to encode PRI, combined with a semantic segmentation network named Sep-RefineNet to identify the PRI features, and finally complete the radar signals deinterleaving by position decoding. The proposed method has an ability to oppose aliasing pulses and missing pulses. It solves the problem that traditional algorithms rely too much on manual experience parameters, and has better robustness for sorting different modulation types of PRI. The proposed method can effectively deinterleave constant PRI, jittered PRI, and staggered PRI within 20% of aliasing pulses and missing pulses, respectively. When the aliasing pulses and missing pulses are both 10%, this method's F1-score is 0.931, which is better than traditional deinterleaving algorithms. Our major contributions are summarized as follows:  
* An FCM is designed to encode and decode the PRI of radar pulse signals. It encodes different PRI's semantic information into the FCM.