@inproceedings{37b44ae5d0994b97ae4e8ca3739f3244,
title = "Drone detection and classification based on radar cross section signatures",
abstract = "In this work, we show how drone detection and classification can be enabled by leveraging a database of radar cross section (RCS) signatures. First, we present a set of measurement results of the RCS of a carbon fiber drone model at 28 GHz. The measurements were performed in an anechoic chamber and provide essential information about the RCS signature of the specific drone. Then, we assess the RCS-based detection probability and the range error by running simulations in urban environments. The drones were positioned at different distances, from 30m to 90m, and the RCS signatures used for the detection and classification were obtained experimentally.",
keywords = "Detection, Propagation, Radar, RCS, UAV",
author = "Vasilii Semkin and Mingsheng Yin and Yaqi Hu and Marco Mezzavilla and Sundeep Rangan",
note = "Funding Information: The work of V. Semkin is supported in part by the Academy of Finland. The work at NYU was performed under award NIST 70NANB17H166 from U.S. Department of Commerce, National Institute of Standards and Technology. Publisher Copyright: {\textcopyright} 2021 IEICE.; 2020 International Symposium on Antennas and Propagation, ISAP 2020 ; Conference date: 25-01-2021 Through 28-01-2021",
year = "2021",
month = jan,
day = "25",
doi = "10.23919/ISAP47053.2021.9391260",
language = "English (US)",
series = "2020 International Symposium on Antennas and Propagation, ISAP 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "223--224",
booktitle = "2020 International Symposium on Antennas and Propagation, ISAP 2020",
}