Jamming Waveform Generation Method Based on Generative Adversarial Network Model

Abstract

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.