Ground-based radar (GBR) has gained significant attention because of its ability to make highly robust, precise measurements in harsh environments, overcoming challenges such as different lighting and weather conditions. However, physical factors like antenna aperture size, limitations of manufacturing cost, imaging calculation speed, and equipment volume lead to inherent limitations in radar image resolution. Radar super-resolution enhances radar image resolution, enabling finer details extracted from observed scenes. In the state-of-the-art, several methods based on deep learning (DL) for radar super-resolution mainly use simulated or indoor datasets, which limits practicality for real-life environments. This paper applies a proposed DL model to real outdoor datasets collected using nano radar. The results show our proposed model's effectiveness on the real data visually. In addition, the results prove that the proposed DL model outperforms the benchmark methods based on different evaluation metrics.