Airborne radar forward‐looking image enhancing algorithm based on generative adversarial networks

Radar forward-looking imaging is gaining significance in various applications like battlefield reconnaissance, target surveillance, and precision guidance. Although synthetic aperture radar techniques provide high azimuth resolution but faced limitations area due to the poor Doppler “left-right” ambiguity problem. Recently, generative adversarial networks have been extensively used for image motion blur removal. This letter proposes an end-to-end enhancing network using produce high-resolution images, improving efficiency, quality of imaging. Compared conventional methods such as deconvolution-based methods, this algorithm eliminates need design iterative processes observation matrix. Simulated real data validate that approach offers robust recovery better performance.