Abstract:

Radar full pulse repeater jamming samples through a digital radio frequency memory device and repeats according to different time sequences. Due to the absence of other modulation features within a single pulse， which is highly similar to echo signals， the feature parameters extraction is difficult， and the differences are inconspicuous in comparison with other jamming recognition. This paper proposes recognition of radar full pulse repeater jamming based on high-order spectrum features， which can suppress Gaussian white noise， contain phase information， and process non-stationary signals. It can be used to extract feature parameters， construct feature vectors， and recognize through machine learning algorithms. Finally， the algorithm is verified through a combination of practical equipment and mathematical simulation. The simulation results show that when the signal-to-noise ratio is greater than 5 dB and 25 dB， the recognition accuracy is close to 80% and 95%， respectively， which verifies the effectiveness of the algorithm and has certain value in engineering application.

Key words: full pulse repeater, jamming recognition, high-order spectrum, feature vector, machine learning

摘要：

雷达全脉冲转发干扰通过数字射频存储器件采样并根据不同的时序转发，由于单个脉冲内不包含其他调制特征，与回波信号具有高度的相似性，相较于其他干扰样式识别，特征参数提取难，差异性不明显。针对此问题，提出一种基于高阶谱特征的雷达全脉冲转发干扰识别方法，根据高阶谱分析抑制高斯白噪声、包含相位信息等适用于处理非平稳信号的特点，提取特征参数构建特征向量，利用机器学习算法进行识别，最后通过实装和数学仿真相结合进行验证。仿真结果表明，在信噪比大于5 dB和25 dB时，识别正确率分别接近80%和95%，验证了算法的有效性，具有一定的工程应用价值。

关键词: 全脉冲转发, 干扰识别, 高阶谱, 特征向量, 机器学习