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Automatic Intrapulse Modulation Classification of Advanced LPI Radar Waveforms

机译：高级LPI雷达波形的自动脉冲内调制分类

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摘要

In this paper, improved signal processing techniques are developed for the analysis and classification of low probability of intercept (LPI) radar waveforms. The intercepted LPI radar signals are classified based on the type of pulse compression waveform. They are classified as linear frequency modulation, nonlinear linear frequency modulation, binary frequency shift keying, polyphase Barker, polyphase P1, P2, P3, P4, and Frank codes. The classification approach is based on the parameters measured from the preprocessed radar signal intercepted by electronic support (ES) or electronic intelligence (ELINT) system. First, signal embedded within the noise is estimated using Wigner Ville distribution to improve the signal-to-noise ratio (SNR). Next, features are extracted using the time-domain and frequency-domain techniques. Furthermore, parameters measured from the fractional Fourier transform are used for the classification. This type of techniques are required in various systems such as ES, electronic attack, radar emitter identification and multi input multi output (MIMO) radar applications. Extensive simulations are carried out with different LPI radar-modulated waveforms corrupted with additive white Gaussian noise of SNR up to -15 dB and impulse noise with 90% of noise density. The proposed algorithm outperforms the existing techniques of classification and can be used under strategic environment.
机译：本文针对低截获概率（LPI）雷达波形的分析和分类开发了改进的信号处理技术。根据脉冲压缩波形的类型对截获的LPI雷达信号进行分类。它们分为线性调频，非线性线性调频，二进制频移键控，多相Barker，多相P1，P2，P3，P4和Frank代码。分类方法基于从电子支持（ES）或电子情报（ELINT）系统拦截的预处理雷达信号中测得的参数。首先，使用Wigner Ville分布估算嵌入在噪声中的信号，以改善信噪比（SNR）。接下来，使用时域和频域技术提取特征。此外，将从分数阶傅里叶变换测量的参数用于分类。各种系统（例如ES，电子攻击，雷达发射器识别和多输入多输出（MIMO）雷达应用）都需要这种技术。对不同的LPI雷达调制波形进行了广泛的仿真，这些波形被SNR高达-15 dB的加性高斯白噪声和具有90％噪声密度的脉冲噪声所破坏。该算法优于现有的分类技术，可在战略环境下使用。

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