Micro-Doppler effect removal in inverse synthetic aperture radar imaging based on UNet

The micro-Doppler (m-D) effect caused by the rotational parts of the targets influences the quality of inverse synthetic aperture radar (ISAR) imaging. In this letter, a novel deep network-assisted method is proposed to reduce the m-D effect in ISAR imaging. The training data, including ISAR images with m-D effect and ISAR images without m-D effect, help the network establish non-linear mapping relationships. The simulated and measured data results show the effectiveness of the proposed method.