Horizontal Attention GAN for Super-Resolution Reconstruction of MIMO Radar Images

Multiple-input multiple-output (MIMO) radar is widely adopted in the fields of forward-looking imaging and target recognition, but its azimuth imaging resolution is fundamentally limited by the size of the physical aperture. Aiming to achieve higher imaging resolution than the theoretical value, an image super-resolution reconstruction method based on the horizontal attention generative adversarial network (HA-GAN) is proposed in this paper. In detail, the horizontal attention mechanism is introduced into the generator to enhance the azimuth resolution, and then the high-resolution (HR) images can be obtained through the adversarial learning between the generator network and the discriminator network. The numerical results demonstrate that the proposed method can break through the theoretical limitation of MIMO azimuth imaging. Moreover, compared to some state-of-the-art methods, the proposed method demonstrates superior performance on sidelobe suppression and super-resolution reconstruction at a low signal-to-noise ratio (SNR). Furthermore, the method’s effectiveness and generalization capability are extensively validated using simulation data, real-world experiments on a millimeter-wave MIMO system, and the public CRUW and RADAL datasets. Overall, the experimental results demonstrate that HA-GAN significantly enhances angular resolution and target recoverability, establishing it as a robust solution for high-precision forward-looking radar imaging.