随着科学技术的不断革新,当前电子战形势日益复杂,雷达面临的电子干扰呈高相参、强欺骗、隐匿性、低功率等特性,严重削弱了雷达的探测和跟踪性能,甚至使其失去作战能力。因此,精准识别雷达面临的有源干扰样式是雷达系统进行针对性干扰抑制的前提。轻量化卷积神经网络(MobileNet)无需人为提取特征便能有效捕获图像中的空间结构信息,在图像处理及分类领域表现优异。文中提出了基于MobileNet 的雷达干扰识别模型,应用对雷达有源干扰的时频特性数据集验证模型的分类效果。实验结果表明,所建立的模型对雷达干扰识别分类的F1-score 高达约0. 9,相比于SIFT 模板匹配、CNN 等模型在各指标上更优,分类效果更好。

With the advancement of technology, the current electronic warfare landscape has become increasingly complex. Radar systems are confronted with electronic interference characterized by high coherence, strong deception, stealthiness and low power. This significantly degrades their detection and tracking capabilities, potentially rendering them combat ineffective. Therefore, the accurate identification of the types of active interference faced by radars is crucial for implementing targeted interference suppression. Lightweight convolutional neural networks (MobileNet), which can effectively capture spatial structural information in images without manual feature extraction, have exhibited excellent performance in image processing and classification. In this paper, a radar interference identification model is proposed based on MobileNet, which is validated by a dataset of time-frequency characteristics of radar active interference. Experimental results reveal that the established model achieves a high F1-score of approximately 0. 9 for radar interference identification and classification, outperforming models such as SIFT template matching and CNN in various metrics, thereby demonstrating superior classification performance.

TN974