Sensing the external complex electromagnetic environment is an important function for cognitive radar, and the concept of cognition has attracted wide attention in the field of radar since it was proposed. In this paper, a novel method based on an idea of multidimensional feature map and convolutional neural network (CNN) is proposed to realize the automatic modulation classification of jamming entering the cognitive radar system. The multidimensional feature map consists of two envelope maps before and after the pulse compression processing and a time-frequency map of the receiving beam signal. Drawing the one-dimensional envelope in a 2-dimensional plane and quantizing the time-frequency data to a 2-dimensional plane, we treat the combination of the three planes (multidimensional feature map) as one picture. A CNN-based algorithm with linear kernel sensing the three planes simultaneously is selected to accomplish jamming classification. The classification of jamming, such as noise frequency modulation jamming, noise amplitude modulation jamming, slice jamming, and dense repeat jamming, is validated by computer simulation. A performance comparison study on convolutional kernels in different size demonstrates the advantage of selecting the linear kernel.

中文翻译：

---

利用卷积神经网络的认知雷达自动干扰调制分类

感知外部复杂的电磁环境是认知雷达的重要功能，自从提出以来，认知的概念就引起了雷达领域的广泛关注。本文提出了一种基于多维特征图和卷积神经网络（CNN）思想的新方法，以实现干扰进入认知雷达系统的自动调制分类。多维特征图由脉冲压缩处理前后的两个包络图和接收波束信号的时频图组成。在二维平面中绘制一维包络，并将时频数据量化为二维平面，我们将三个平面的组合（多维特征图）视为一张图片。选择具有线性核同时检测三个平面的基于CNN的算法来完成干扰分类。通过计算机仿真验证了干扰的分类，例如噪声频率调制干扰，噪声幅度调制干扰，切片干扰和密集重复干扰。对不同大小的卷积核的性能比较研究证明了选择线性核的优势。