| DOI | Resolve DOI: https://doi.org/10.1109/SAS58821.2023.10253987 |
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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 of Canada. Aerospace
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| Format | Text, Article |
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| Conference | 2023 IEEE Sensors Applications Symposium (SAS), July 18-20, 2023, Ottawa, ON, Canada |
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| Subject | radar; clutter; drone classification; drone detection; microwave; multi-radar; HERM; V-band; wavelet |
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| Abstract | Radars are all-weather instruments that operate in various frequency bands and therefore are useful for detecting and monitoring unmanned aerial vehicles (UAV) such as drones. Majority of the research in drone classification are carried out in controlled, clutter-free indoor environments. This work presents drone detection data collected using a 66 GHz research radar from aiRadar Inc in both indoor and outdoor environments. Time-frequency analysis techniques, namely short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are used to extract micro-Doppler signatures of the rotating propellers. A qualitative comparison was done for the radar returns of a quadcopter and an octocopter, both for indoors and outdoors environment. It was discovered that the spectrograms and scalograms are noisier outdoors compared to indoors. It was also determined that the scalograms from CWT produced more distinctive features compared to STFT. |
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| Publication date | 2023-07-18 |
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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 | aab17d81-2ea3-4956-91c9-ce359770cacc |
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| Record created | 2024-11-12 |
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| Record modified | 2024-11-12 |
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