Abstract

Detection and classification of Low Probability of Interception (LPI) radar signals is one of the most important challenges in electronic warfare (EW), since there are limited methods for identifying these type of signals. In this paper, a radar waveform automatic identification system for detecting and classifying LPI radar is studied, and accordingly we propose a method based on deep learning networks to detect and classify LPI radar waveforms. To this end, the GoogLeNet architecture as one of the well-known convolutional neural networks (CNN) is utilized. We employ the Short Time Fourier Transform (STFT) for time-frequency analysis in order to construct the entry image for proposed method 1,2 (improved the GoogLeNet and AlexNet networks) to recognize offline training and online recognition. After the training procedure with the supervised data sets the proposed method 1,2 can detect and classify nine modulation types of LPI radar, including LFM, poly-phase (P1, P2, P3, P4) and poly-time (T1, T2, T3, T4) waveforms. The numerical results for proposed method 1, show considerable accuracies up to 98.7% at the SNR level of –15 dB, which outperforms the existing methods.

中文翻译：

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基于深度学习的拦截雷达信号检测和分类概率低的方法

摘要

低拦截概率（LPI）雷达信号的检测和分类是电子战（EW）中最重要的挑战之一，因为识别这些类型信号的方法有限。本文研究了一种用于LPI雷达检测和分类的雷达波形自动识别系统，据此提出了一种基于深度学习网络的LPI雷达波形检测和分类方法。为此，利用了作为众所周知的卷积神经网络（CNN）之一的GoogLeNet架构。我们采用短时傅立叶变换（STFT）进行时频分析，以便为建议的方法1,2（改进的GoogLeNet和AlexNet网络）构建进入图像，以识别离线训练和在线识别。经过带有监督数据集的训练过程后，建议的方法1,2可以检测和分类LPI雷达的9种调制类型，包括LFM，多相（P1，P2，P3，P4）和多时（T1，T2， T3，T4）波形。提议的方法1的数值结果显示，在–15 dB的SNR级别上，可观的精度高达98.7％，优于现有方法。