A Radar Signal Deinterleaving Method Based on Semantic Segmentation with Neural Network

TL;DR

This paper introduces a novel radar signal deinterleaving method using semantic segmentation neural networks, which improves robustness and efficiency in complex environments without extensive parameter searching or target-specific training.

Contribution

The study presents a new semantic segmentation-based deinterleaving approach that outperforms traditional and existing neural network methods in robustness and adaptability.

Findings

High robustness to pulse loss and noise pulses.

Recurrent neural networks outperform convolutional neural networks in this task.

No need for data digitization or target-specific network training.

Abstract

Radar signal deinterleaving is an important part of electronic reconnaissance. This study proposes a new radar signal deinterleaving method based on semantic segmentation, which we call "semantic segmentation deinterleaving" (SSD). We select representative sequence modeling neural network (NN) architectures and input the difference of time of arrival of the pulse stream into them. According to semantics contained in different radar signal types, each pulse in the pulse stream is marked according to the category of semantics contained, and radar signals are deinterleaved. Compared to the traditional deinterleaving method, the SSD method can adapt to complex pulse repetition interval (PRI) modulation environments without searching the PRI or PRI period. Multiple rounds of search and merging operation are not required for radar signals with multiple pulses in a period. Compared to other deinterleaving methods based on NNs, the SSD method does not need to digitize the data and train a network for each target type. The SSD method also does not need to iterate input and output data. The proposed method has high robustness to pulse loss and noise pulses. This research also shows that recurrent NNs still have more advantages than convolutional NNs in this sequence modeling task.