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OA

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.