Unmanned Aerial Vehicle Classification Using Neural Neworks and Radar Digital Twins: UAV Classification Using Neural Networks and Radar Digital Twins
Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as versatile tools with a wide range of applications across various fields. UAV technology has rapidly evolved, leading to its adoption in civilian and commercial sectors. UAVs offer numerous benefits, including cost-effectiveness, flexibility, and accessibility, making them invaluable assets in various industries such as precision agriculture, environmental monitoring, disaster response, and infrastructure inspection. They provide real-time aerial data and imagery, enabling farmers to optimize crop management, conservationists to monitor ecosystems, and emergency responders to assess disaster-affected areas <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref>. Additionally, UAVs play a crucial role in contactless vital sign monitoring <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> and in entertainment and filmmaking, revolutionizing aerial cinematography and photography <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref>. With their versatility and accessibility, UAVs continue to drive innovation and efficiency in industries worldwide. However, the increased accessibility and availability of UAVs have also raised concerns about their potential use in illegal activities and terrorist attacks <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref>, <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref>, <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref>, <xref ref-type="bibr" rid="ref7" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[7]</xref>. This highlights the importance of identifying and classifying drones for safety and security purposes.
Authors
- Ahmad Ansariyan
- Omar M. Ramahi (ORCID: https://orcid.org/0000-0002-9403-0029)
- George Shaker (ORCID: https://orcid.org/0000-0002-1450-2138)
- Michael M.Y.R. Riad (ORCID: https://orcid.org/0000-0002-3127-2186)
- Ahmed N. Sayed (ORCID: https://orcid.org/0000-0003-3821-0487)
- Laila Salman
Institutions
- University of Waterloo (CA)
- Ansys (Canada) (CA)
Publication Details
- Journal
- IEEE Microwave Magazine
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1109/mmm.2024.3444529
- Citations
- 9
- Primary Topic
- Advanced Optical Sensing Technologies
- Type
- article
- Field-Weighted Citation Impact
- 4.56
Funders
- Natural Sciences and Engineering Research Council of Canada