无线电工程2024，Vol.54Issue(7)：1652-1659,8.DOI:10.3969/j.issn.1003-3106.2024.07.007
一种基于ResNet的雷达弱小目标检测方法
A Detection Method for Radar Weak Targets Based on ResNet
摘要
Abstract
In order to solve the problem that Constant False Alarm Rate(CFAR)detection algorithm is difficult to detect radar weak targets,the target detection method based on Convolutional Neural Network(CNN)is studied.Taking full advantage of the excellent performance of neural networks in feature extraction,a radar weak target detection method based on the Residual Network(ResNet)block is proposed.This method breaks through the framework of traditional methods using only amplitude information for the object detection,and the phase features in radar echo data are fully mined as the basis for neural network object classification detection.According to experiments,the proposed method can still achieve a detection probability of over 50％even when the signal-to-noise ratio of the target echo is only-7 dB.Moreover,as the signal-to-noise ratio decreases,the superiority of the proposed method becomes more apparent.关键词
恒虚警率检测/残差网络/弱小目标检测Key words
CFAR detection/ResNet/weak target detection分类
信息技术与安全科学引用本文复制引用
邱明劼,张鹏,汪圣利..一种基于ResNet的雷达弱小目标检测方法[J].无线电工程,2024,54(7):1652-1659,8.基金项目
国家自然科学基金(62101261)National Natural Science Foundation of China(62101261) （62101261）
