Online unsupervised generative learning framework based radar jamming waveform design - Yuzheng Sun; Shuaige Gong; Yu Mao; Yang‐Yang Dong; Chun‐Xi Dong (IET Radar, Sonar & Navigation, 2023)
Abstract The jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the radar signal power spectrum density (PSD), a novel jamming waveform design method with an online unsupervised generative learning framework is proposed. The proposed framework consists of two modules: the waveform online generation module and the waveform unsupervised optimisation module. For the waveform online generation module, the well‐designed neural network (NN) is used to generate the jamming waveform. While for the waveform unsupervised iterative optimisation module, two loss functions are developed as two learning tasks to guide back‐propagation online. Given transmit power, the jamming PSD is designed adaptively to focus on the radar band and fit the radar PSD well, which improves the receiver in‐band jamming‐to‐signal ratio (JSR). It is worth noting that NN is end‐to‐end trained online without extensive prior training data. The framework can generate jamming waveforms within reasonable physical parameter ranges, and numerical experiments have shown that the designed jamming waveform performed better jamming effects on radar target detection in comparison with the traditional one.