The role of target signatures in bird-drone classification

George Atkinson; Mohammed Jahangir; Daniel White; Joseph P.  Wayman; Chris Baker; Jon Sadler; Jim Reynolds; Michail Antoniou

doi:10.1109/RadarConf2351548.2023.10149665

The role of target signatures in bird-drone classification

George Atkinson^*, Mohammed Jahangir, Daniel White, Joseph P. Wayman, Chris Baker, Jon Sadler, Jim Reynolds, Michail Antoniou

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Mapping and characterising low to medium airspace in an urban setting using radar presents significant challenges, especially for low observable targets such as drones and birds. This paper presents some examples of micro-Doppler signatures which make up part of a larger dataset of control drone, control bird, and opportune bird signatures measured with a pair of L-band staring radars installed at an urban location at the University of Birmingham. The results of multiple measurements over a two-year period are used to facilitate the classification of birds and drones, and the results of a machine learning classifier on the collected data is shown.

Original language	English
Title of host publication	2023 IEEE Radar Conference (RadarConf23)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	6
ISBN (Electronic)	9781665436694
ISBN (Print)	9781665436700 (PoD)
DOIs	https://doi.org/10.1109/RadarConf2351548.2023.10149665
Publication status	Published - 21 Jun 2023
Event	2023 IEEE Radar Conference, RadarConf23 - San Antonia, United States Duration: 1 May 2023 → 5 May 2023

Publication series

Name	Proceedings of the IEEE Radar Conference
Publisher	IEEE
ISSN (Print)	1097-5764
ISSN (Electronic)	2375-5318

Conference

Conference	2023 IEEE Radar Conference, RadarConf23
Country/Territory	United States
City	San Antonia
Period	1/05/23 → 5/05/23

Bibliographical note

This paper was first presented at the 2023 IEEE Radar Conference on 5th May 2023.

Funding Information:
The work was funded by the UK National Quantum Technology Hub in Sensing and Timing (EP/T001046/1) and the EPSRC MEFA (EP/T011068/1) projects. The authors are grateful to Aveillant Ltd for helping with installing the radar and their ongoing engagement with the research work supported by the testbed.

Publisher Copyright:
© 2023 IEEE.

Keywords

L-Band
staring radar
urban surveillance

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Instrumentation

Access to Document

10.1109/RadarConf2351548.2023.10149665Licence: None: All rights reserved

AtkinsonG2023Role
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 1.48 MBLicence: None: All rights reserved

1 Finished
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UK National Quantum Technology Hub in Sensing and Timing
Attallah, M., Jones, R., Metje, N., Constantinou, C., Roberts, C., Faramarzi, A., Singh, Y., Holynski, M., Bongs, K., Baker, C. & Antoniou, M.
Engineering & Physical Science Research Council
1/12/19 → 30/11/24
Project: Research Councils
MEFA: Mapping and Enabling Future Airspace
Reynolds, J., Antoniou, M., Sadler, J., Baker, C. & Baker, C.
Engineering & Physical Science Research Council
1/04/20 → 31/03/24
Project: Research Councils

Cite this

Atkinson, G., Jahangir, M., White, D., Wayman, J. P., Baker, C., Sadler, J., Reynolds, J., & Antoniou, M. (2023). The role of target signatures in bird-drone classification. In 2023 IEEE Radar Conference (RadarConf23) Article 10149665 (Proceedings of the IEEE Radar Conference). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/RadarConf2351548.2023.10149665

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abstract = "Mapping and characterising low to medium airspace in an urban setting using radar presents significant challenges, especially for low observable targets such as drones and birds. This paper presents some examples of micro-Doppler signatures which make up part of a larger dataset of control drone, control bird, and opportune bird signatures measured with a pair of L-band staring radars installed at an urban location at the University of Birmingham. The results of multiple measurements over a two-year period are used to facilitate the classification of birds and drones, and the results of a machine learning classifier on the collected data is shown.",

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Atkinson, G, Jahangir, M , White, D , Wayman, JP, Baker, C, Sadler, J , Reynolds, J & Antoniou, M 2023, The role of target signatures in bird-drone classification. in 2023 IEEE Radar Conference (RadarConf23)., 10149665, Proceedings of the IEEE Radar Conference, Institute of Electrical and Electronics Engineers (IEEE), 2023 IEEE Radar Conference, RadarConf23, San Antonia, United States, 1/05/23. https://doi.org/10.1109/RadarConf2351548.2023.10149665

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AU - Reynolds, Jim

AU - Antoniou, Michail

N1 - This paper was first presented at the 2023 IEEE Radar Conference on 5th May 2023. Funding Information: The work was funded by the UK National Quantum Technology Hub in Sensing and Timing (EP/T001046/1) and the EPSRC MEFA (EP/T011068/1) projects. The authors are grateful to Aveillant Ltd for helping with installing the radar and their ongoing engagement with the research work supported by the testbed. Publisher Copyright: © 2023 IEEE.

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N2 - Mapping and characterising low to medium airspace in an urban setting using radar presents significant challenges, especially for low observable targets such as drones and birds. This paper presents some examples of micro-Doppler signatures which make up part of a larger dataset of control drone, control bird, and opportune bird signatures measured with a pair of L-band staring radars installed at an urban location at the University of Birmingham. The results of multiple measurements over a two-year period are used to facilitate the classification of birds and drones, and the results of a machine learning classifier on the collected data is shown.

AB - Mapping and characterising low to medium airspace in an urban setting using radar presents significant challenges, especially for low observable targets such as drones and birds. This paper presents some examples of micro-Doppler signatures which make up part of a larger dataset of control drone, control bird, and opportune bird signatures measured with a pair of L-band staring radars installed at an urban location at the University of Birmingham. The results of multiple measurements over a two-year period are used to facilitate the classification of birds and drones, and the results of a machine learning classifier on the collected data is shown.

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The role of target signatures in bird-drone classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Projects

UK National Quantum Technology Hub in Sensing and Timing

MEFA: Mapping and Enabling Future Airspace

Cite this