Learning-Based Realtime Synthetic Aperture Radar Imaging for Embedded System on Satellite

Spaceborne Synthetic Aperture Radar (SAR), due to its ability of all-weather and all-day sensing, is extensively utilized across various fields. Realtime imaging on satellite is highly advantageous as it significantly reduces the communication cost and delay between satellite and ground station, making it exceptionally suitable for time-sensitive applications like maritime search and rescue. However, the limited computing power of embedded system on satellite, together with the substantial computational demands of traditional imaging algorithms, pose challenges for realtime imaging. To address these issues, a lightweight learning-based model with adjustable complexity is proposed for realtime imaging. Furthermore, we collect a large amount of real-world echo data from satellite and construct the first large-scale dataset, for training and evaluating the learning-based SAR imaging task. Experimental results and evaluation in the embedded system show that, the proposed learning-based model achieves up to 19× speedup than traditional algorithm.