| DOI | Resolve DOI: https://doi.org/10.1109/I2MTC53148.2023.10176027 |
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| Author | Search for: Lam, Ian; Search for: Pant, Shashank1ORCID identifier: https://orcid.org/0000-0003-3271-5011; Search for: Manning, Max; Search for: Kubanski, Michael; Search for: Fox, Peter; Search for: Rajan, Sreeraman; Search for: Patnaik, Prakash1ORCID identifier: https://orcid.org/0000-0002-2122-3882; Search for: Balaji, Bhashyam |
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| Affiliation | - National Research Council Canada. Aerospace
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| Format | Text, Article |
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| Conference | 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May 22-25, 2023, Kuala Lumpur, Malaysia |
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| Subject | radar; drone classification; drone detection; microwave; multi-radar; HERM; V-band; wavelet; time-frequency analysis; continuous wavelet transforms; airborne radar; radar detection; rotors; receivers; autonomous aerial vehicles |
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| Abstract | Detection and classification of unmanned aerial vehicles (UAV), namely drones, is an active area of research. Radars are all-weather instrument that operate in various frequency bands and are used for monitoring drone activities. However, little experimental work has been done with radars that operate in the V-band (40 GHz to 75 GHz) portion of the electromagnetic spectrum. This paper presents drone detection data collected using a 66 GHz research radar from aiRadar Inc. Micro-Doppler signatures of the rotating drone rotors are extracted using short-time Fourier transform (STFT) and continuous wavelet transforms (CWT). It was determined that the complex Morlet wavelet provides more detailed micro-Doppler features that can be used for classifying different drones. |
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| Publication date | 2023-05-22 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 578e0fbc-154e-4307-8534-4e8c5e0fd0aa |
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| Record created | 2024-11-18 |
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| Record modified | 2024-11-18 |
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