论文标题
雷达信号生成的生成对抗网络
Generative Adversarial Network for Radar Signal Generation
论文作者
论文摘要
基于雷达的方法，用于隐藏对象检测人类和无缝集成到安全性和访问控制系统中的主要障碍是收集高质量雷达信号数据的困难。生成对抗网络（GAN）在图像和音频处理领域的数据生成应用中显示了有望。因此，本文提出了用于在雷达信号生成中应用的GAN的设计。使用三个隐藏对象类（没有对象，大对象和小对象）上的有限差分时间域（FDTD）方法收集的数据被用作训练数据来训练gan，以为每个类生成雷达信号样本。提出的GAN生成了雷达信号数据，这与定性人类观察者的训练数据没有区别。
A major obstacle in radar based methods for concealed object detection on humans and seamless integration into security and access control system is the difficulty in collecting high quality radar signal data. Generative adversarial networks (GAN) have shown promise in data generation application in the fields of image and audio processing. As such, this paper proposes the design of a GAN for application in radar signal generation. Data collected using the Finite-Difference Time-Domain (FDTD) method on three concealed object classes (no object, large object, and small object) were used as training data to train a GAN to generate radar signal samples for each class. The proposed GAN generated radar signal data which was indistinguishable from the training data by qualitative human observers.
