For the issues of unclear jamming effects in traditional intra-pulse jamming waveform generation and the lagging jamming false targets caused by reconnaissance windowing in relay jamming, this paper proposes a jamming waveform generation method based on Generative Adversarial Network (GAN) Models. This model, trained on GAN, generates jamming waveforms driven by jamming effects. Using minimal segments of radar signal headers as input, the jamming waveform generation model reversely predicts and generates complete radar jamming signals based on jamming effects. By reducing the reconnaissance windowing for the jamming side and pre-modulating jamming waveforms based on anticipated jamming effects, this method possesses the capability to achieve transcendental jamming (where the peak value of the jamming false target formed after the pulse compression of jamming waveforms occurs before the peak value of the real target). Experimental results indicate that the radar jamming waveforms generated based on GAN Models can achieve transcendental jamming, increasing the difficulty of radar jamming signal recognition.

Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto‐encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE‐based jamming suppression auto‐encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference‐related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data.

This paper designs and simulates an interference metasurface capable of generating range profiles of false targets, by manipulating the electromagnetic reflection properties of the surface through control codes. Deep Deterministic Policy Gradient (DDPG) algorithm is applied to generate multiple sets of control codes that result in high similarity between the range profiles produced by the metasurface and those of real targets, as measured by the Pearson product moment correlation coefficient (PPMCC). Furthermore, the encoded metasurface is able to maintain similarity with the range profiles of real targets even when the incident wave's azimuthal angle is altered, thus achieving dynamic interference angle manipulation.