Dop‐NET: a micro‐Doppler radar data challenge. Issue 11 (1st May 2020)

• Record Type: Journal Article
• Authors: Ritchie, M.; Capraru, R.; Fioranelli, F.
• Is Part Of: Electronics letters. Volume 56:Issue 11(2020)
• Page Start: 568
• Page End: 570
• Publication Date: 2020-05-01
• Subjects: learning (artificial intelligence) -- radar signal processing -- CW radar -- Doppler radar -- signal classification; signal processing -- compact RF sensors -- Dop‐NET radar microDoppler database -- machine learning communities -- radar microDoppler signatures -- microDoppler classification techniques -- Dop‐NET database -- microDoppler radar data challenge -- radar sensors -- dynamic hand gesture recognition -- continuous wave radar module -- frequency 24.0 GHz; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2019.4153
• Languages: English
• ISSNs: 0013-5194

Abstract

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.