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Frequency-modulated continuous-wave (FMCW) radar is a promising sensortechnology for indoor drones as it provides range, angular as well asDoppler-velocity information about obstacles in the environment. Recently, deeplearning approaches have been proposed for processing FMCW data, outperformingtraditional detection techniques on range-Doppler or range-azimuth maps.However, these techniques come at a cost; for each novel task a deep neuralnetwork architecture has to be trained on high-dimensional input data,stressing both data bandwidth and processing budget. In this paper, weinvestigate unsupervised learning techniques that generate low-dimensionalrepresentations from FMCW radar data, and evaluate to what extent theserepresentations can be reused for multiple downstream tasks. To this end, weintroduce a novel dataset of raw radar ADC data recorded from a radar mountedon a flying drone platform in an indoor environment, together with ground truthdetection targets. We show with real radar data that, utilizing our learnedrepresentations, we match the performance of conventional radar processingtechniques and that our model can be trained on different input modalities suchas raw ADC samples of only two consecutively transmitted chirps.

FMCW Radar Sensing for Indoor Drones Using Learned Representations