空天防御2026，Vol.9Issue(1)：46-51,114,7.
对抗样本攻击下提高雷达智能识别模型稳健性的算法研究
Robustness of Radar Intelligent Recognition Models Under Adversarial Samples Attacks
沈曈 1陈敬贤 1钟平2
作者信息
- 1. 上海机电工程研究所,上海 201109
- 2. 自动目标识别重点实验室(长沙),湖南 长沙 410073
- 折叠
摘要
Abstract
Addressing the problem of insufficient robustness of the intelligent recognition model of radar High Resolution Range Profile(HRRP)under adversarial sample attacks,a lightweight enhancement method was proposed in this study.Firstly,a comprehensive analysis was conducted using the Fast Gradient Sign Method(FGSM),Projected Gradient Descent(PGD),and a black-box migration attack to assess the vulnerability of the lightweight Convolutional Neural Network(CNN).Secondly,a cascaded defense strategy of"fast adversarial training+input denoising auto encoder+post-anomaly detection"was established.Finally,countermeasure experiments were carried out using three types of air targets and 6,000 sets of measured samples.The results show that this strategy can reduce the attack success rate to 9.2%,sacrificing only 2.1 percentage point of the cleaning accuracy,and increasing inference delay by less than 20%.It achieves a stable balance among model size,real-time performance,and robustness,providing a practical solution for the anti-interference design in radar-intelligent recognition systems.关键词
雷达目标识别/对抗样本/雷达智能识别模型稳健性/对抗训练Key words
radar target recognition/adversarial examples/robustness of radar intelligent recognition model/adversarial training分类
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沈曈,陈敬贤,钟平..对抗样本攻击下提高雷达智能识别模型稳健性的算法研究[J].空天防御,2026,9(1):46-51,114,7.
