现代战争中雷达信号日趋复杂，如何快速准确地从种类繁多、数据量庞大的雷达检测数据中，获取目标航迹的类别信息，为战场指挥提供准确有效的信息是当前急需解决的难题。传统基于人的经验认知的雷达目标航迹识别方法已经无法有效应对瞬息万变的战场和海量数据。根据实际雷达数据特点，提出了使用对数的雷达航迹预处理方法，并构建了基于卷积神经网络的深度学习模型，实现了对雷达对抗中的目标航迹的识别与检测。基于模拟生成的雷达目标航迹数据对提出的数据预处理方法和构建的模型进行测试；实验表明，所提出的方法能很好地实现对目标航迹的检测与识别。

A large number of various radar signals in modern warfare make the electromagnetic environment more and more complex. It is urgent to quickly and accurately obtain the category information of the target track from a large number of radar data, and provide accurate and effective information for the battlefield command. The traditional radar-based target recognition method based on human experience or cognition is unable to effectively cope with the ever-changing battlefield and massive data. Based on the characteristics of actual radar data, this paper proposes a logarithmic preprocessing method and constructs a deep learning model based on convolutional neural network. The deep learning model realizes the recognition and detection of the target track in radar confrontation. The built model is tested based on the radar target track data generated by the simulation. Experiments show that the model can effectively detect and identify the target track.