Generation of a synthetic aperture radar deception jamming signal based on a deep echo inversion network

Existing methods for generating synthetic aperture radar (SAR) deception jamming signals have slow speed, low imaging quality, and insufficient intelligence in complex electromagnetic environments. This paper proposes a deep learning-based SAR deception jamming signal generation method based on deep echo inversion Unet (DEIUnet). This method has high speed and provides high-image quality of the interference signal. A Swin Next (SN) block is proposed to combine local and non-local information in the image and echo data. The Unet structure consists of SN blocks, and a residual connection is used as the jump connection to fuse the multi-scale feature information from the echo and image data. PixelShuffle is utilised for up-sampling to generate high-quality echo data. The experimental results on MSTAR and Sentinel-1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion. The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method.