@inproceedings{876bf42c3efd4a3bad1d346d41007783,
title = "FMCW Radar Sensing for Indoor Drones Using Variational Auto-Encoders",
abstract = "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.",
keywords = "Deep learning, Drone navigation, Indoor sensing, Variational autoencoder, Velocity and angle estimation",
author = "Ali Safa and Tim Verbelen and Ozan Catal and \{Van De Maele\}, Toon and Matthias Hartmann and Bart Dhoedt and Andre Bourdoux",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Radar Conference, RadarConf23 ; Conference date: 01-05-2023 Through 05-05-2023",
year = "2023",
doi = "10.1109/RadarConf2351548.2023.10149738",
language = "English",
series = "Proceedings of the IEEE Radar Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 Ieee Radar Conference, Radarconf23",
address = "United States",
}