Radar Data Simulation Using Deep Generative Networks

Radar data generation plays an important role in radar applications. Radar data generation method contains simulation based on statistic model and electromagnetic simulation. These methods are sensitivity to model error, and the electromagnetic simulation always faces heavy calculation. In this paper, a method based on deep generative model was proposed in which a generative model can be trained with only few data samples, and radar data can be generated rapidly without heavy calculation. This method was applied to generate radar HRRP, and the result shows that target HRRP can be generated, and the generated HRRPs are similar to real radar data in visual and in statistic domain, and the generated HRRP can be used to eliminate the effect of imbalance problems.