Ritchie, M;
Capraru, R;
Fioranelli, F;
(2020)
DopNET: A Micro-Doppler Radar Data Challenge.
Electronics Letters
, 56
(11)
pp. 568-570.
10.1049/el.2019.4153.
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Abstract
Radar sensors have a new growing application area of dynamic hand gesture recognition. Traditionally radar systems are considered to be very large, complex and focused on detecting targets at long ranges. With modern electronics and signal processing it is now possible to create small compact RF sensors that can sense subtle movements over short ranges. For such applications, access to comprehensive databases of signatures is critical to enable the effective training of classification algorithms and to provide a common baseline for benchmarking purposes. This Letter introduces the Dop-NET radar micro-Doppler database and data challenge to the radar and machine learning communities. Dop-NET is a database of radar micro-Doppler signatures that are shareable and distributed with the purpose of improving micro-Doppler classification techniques. A continuous wave 24 GHz radar module is used to capture the first contributions to the Dop-NET database and classification results based on discriminating these hand gestures as shown.
| Type: | Article |
|---|---|
| Title: | DopNET: A Micro-Doppler Radar Data Challenge |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1049/el.2019.4153 |
| Publisher version: | https://doi.org/10.1049/el.2019.4153 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10092360 |
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