This paper investigates unsupervised learning of low-dimensional representations from FMCW radar data, which can be used for multiple downstream tasks in a drone navigation context. To this end, we release a first-of-its-kind dataset of raw radar ADC data recorded from a radar mounted on a flying drone in an indoor environment, together with ground truth detection targets. We show that, by utilizing our learned representations, we match the performance of conventional radar processing techniques while training our models on different input modalities such as range-doppler maps, range-azimuth maps, or raw ADC samples of only two consecutively transmitted chirps.