基于时频图深度学习的雷达动目标检测与分类

雷达动目标检测技术一直是雷达信号处理领域中的关键技术，而传统的雷达动目标检测技术仅适用于匀速运动目标，检测性能有限。针对该问题提出一种基于卷积神经网络(CNN)时频图处理的雷达动目标检测方法，通过从雷达动目标回波中提取多普勒频移信息，然后利用短时傅里叶变换转换为时频图，输入卷积神经网络，进行深度特征学习，进而实现检测和分类的目的。仿真数据验证表明，所提方法能够有效检测和区分匀速、匀变速运动以及微动目标，稳健性高，与传统动目标检测方法相比具有显著优势。

Radar detection and classification of moving target using deep convolutional neural networks on time-frequency graphs

Radar moving target detection technology is always a key technology in the field of radar signal processing. The traditional radar moving target detection technology is only suitable for uniformly moving targets, and the detection performance is limited. This paper proposes a radar Moving Target Detection(MTD) method based on Convolutional Neural Network(CNN) time-frequency processing. It extracts the Doppler shift information from the radar moving target echo, and then transforms it into time-frequency graph with short-time Fourier transform. After inputting the time-frequency graph into the CNN, the characteristic learning is performed to achieve the purpose of detection and classification. Simulation shows that this method is superior to traditional moving target detection methods.