Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is -6 dB.

中文翻译：

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基于多个图像特征的LPI雷达波形识别。

在电子智能系统中，实时检测和分类被拦截的有噪声的LPI（低拦截概率）雷达信号的调制类型是一种必不可少的生存技术。大多数雷达信号已被设计为具有LPI特性。因此，LPI雷达波形识别技术（LWRT）最近受到越来越多的关注。在本文中，我们提出了一种具有两种不同特征提取结构的多特征图像联合决策（MFIJD）模型，该模型可以完全提取像素特征以获得大多数LPI雷达信号的非平稳特征的每个特征图像的预分类结果。该模型的核心技术是结合短时自相关特征图像，双短时自相关特征图像和原始信号时频图像（TFI）同时输入到混合模型分类器中，适用于非平稳信号，具有较高的通用性。我们通过模拟本文定义的11种信号并生成训练集和测试集来证明MFIJD的性能。与文献的比较表明，该方法不仅对LPI雷达信号具有很高的通用性，而且在低SNR（信噪比）环境下更适合LPI雷达波形识别。当SNR为-6 dB时，该方法的总识别率达到87.7％。我们通过模拟本文定义的11种信号并生成训练集和测试集来证明MFIJD的性能。与文献的比较表明，该方法不仅对LPI雷达信号具有很高的通用性，而且在低SNR（信噪比）环境下更适合LPI雷达波形识别。当SNR为-6 dB时，该方法的总识别率达到87.7％。我们通过模拟本文定义的11种信号并生成训练集和测试集来演示MFIJD的性能。与文献的比较表明，该方法不仅对LPI雷达信号具有很高的通用性，而且在低SNR（信噪比）环境下更适合LPI雷达波形识别。当SNR为-6 dB时，该方法的总识别率达到87.7％。