Drone detection and classification based on radar cross section signatures

Vasilii Semkin; Mingsheng Yin; Yaqi Hu; Marco Mezzavilla; Sundeep Rangan

doi:10.23919/ISAP47053.2021.9391260

Drone detection and classification based on radar cross section signatures

Vasilii Semkin^*
, Mingsheng Yin
, Yaqi Hu
, Marco Mezzavilla
, Sundeep Rangan

^*Corresponding author for this work

NYU Tandon School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

38 Citations (Scopus)

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.

Original language	English
Title of host publication	2020 International Symposium on Antennas and Propagation, ISAP 2020
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	223-224
ISBN (Electronic)	978-4-88552-326-7
ISBN (Print)	978-1-7281-5909-6
DOIs	https://doi.org/10.23919/ISAP47053.2021.9391260
Publication status	Published - 25 Jan 2021
MoE publication type	A4 Article in a conference publication
Event	2020 International Symposium on Antennas and Propagation, ISAP 2020: Online - Virtual, Osaka, Japan Duration: 25 Jan 2021 → 28 Jan 2021

Conference

Conference	2020 International Symposium on Antennas and Propagation, ISAP 2020
Country/Territory	Japan
City	Osaka
Period	25/01/21 → 28/01/21

Keywords

Detection
Propagation
Radar
RCS
UAV

Access to Document

10.23919/ISAP47053.2021.9391260

Cite this

@inproceedings{6418214fc241481cbb1e160e9b6ba97e,

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 : Online ; Conference date: 25-01-2021 Through 28-01-2021",

year = "2021",

month = jan,

day = "25",

doi = "10.23919/ISAP47053.2021.9391260",

language = "English",

isbn = "978-1-7281-5909-6",

pages = "223--224",

booktitle = "2020 International Symposium on Antennas and Propagation, ISAP 2020",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

}

Semkin, V, Yin, M, Hu, Y, Mezzavilla, M & Rangan, S 2021, Drone detection and classification based on radar cross section signatures. in 2020 International Symposium on Antennas and Propagation, ISAP 2020., 9391260, IEEE Institute of Electrical and Electronic Engineers, pp. 223-224, 2020 International Symposium on Antennas and Propagation, ISAP 2020, Osaka, Japan, 25/01/21. https://doi.org/10.23919/ISAP47053.2021.9391260

Drone detection and classification based on radar cross section signatures. / Semkin, Vasilii; Yin, Mingsheng; Hu, Yaqi et al.
2020 International Symposium on Antennas and Propagation, ISAP 2020. IEEE Institute of Electrical and Electronic Engineers, 2021. p. 223-224 9391260.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Drone detection and classification based on radar cross section signatures

AU - Semkin, Vasilii

AU - Yin, Mingsheng

AU - Hu, Yaqi

AU - Mezzavilla, Marco

AU - Rangan, Sundeep

N1 - 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: © 2021 IEICE.

PY - 2021/1/25

Y1 - 2021/1/25

N2 - 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.

AB - 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.

KW - Detection

KW - Propagation

KW - Radar

KW - RCS

KW - UAV

UR - https://www.scopus.com/pages/publications/85104496955

U2 - 10.23919/ISAP47053.2021.9391260

DO - 10.23919/ISAP47053.2021.9391260

M3 - Conference article in proceedings

AN - SCOPUS:85104496955

SN - 978-1-7281-5909-6

SP - 223

EP - 224

BT - 2020 International Symposium on Antennas and Propagation, ISAP 2020

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 2020 International Symposium on Antennas and Propagation, ISAP 2020

Y2 - 25 January 2021 through 28 January 2021

ER -

Drone detection and classification based on radar cross section signatures

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this