Concealed Electronic Countermeasures of Radar Signal with Adversarial Examples
This work addresses the need for more concealed radar signal jamming in military applications, representing an incremental advancement by extending AI-based attacks from time domain to time-frequency scenarios.
The paper tackles the problem of making electronic countermeasures against radar signals less detectable by focusing on time-frequency images classification, proposing an attack pipeline and algorithms that achieve a high success rate in experiments.
Electronic countermeasures involving radar signals are an important aspect of modern warfare. Traditional electronic countermeasures techniques typically add large-scale interference signals to ensure interference effects, which can lead to attacks being too obvious. In recent years, AI-based attack methods have emerged that can effectively solve this problem, but the attack scenarios are currently limited to time domain radar signal classification. In this paper, we focus on the time-frequency images classification scenario of radar signals. We first propose an attack pipeline under the time-frequency images scenario and DITIMI-FGSM attack algorithm with high transferability. Then, we propose STFT-based time domain signal attack(STDS) algorithm to solve the problem of non-invertibility in time-frequency analysis, thus obtaining the time-domain representation of the interference signal. A large number of experiments show that our attack pipeline is feasible and the proposed attack method has a high success rate.