TY  - GEN
CY  - Boston, MA, USA
T3  - IEEE Radar Conference (RadarConf)
TI  - Micro-Doppler Gesture Recognition using Doppler, Time and Range Based Features
SN  - 2375-5318
PB  - IEEE
N2  - This paper presents micro-Doppler analysis and classification results from radar measurements of various hand gestures. A new database of 6 individuals completing 4 separate gestures with over 3,000 repetitions was recorded using a 24 GHz Ancortek radar system. The micro-Doppler signatures from these gestures were generated, features extracted and multiple different classifiers applied to this gesture data. A typical micro-Doppler classification process aims to use either a single range bin of data, average over a series of range bins or align all the target signal to a single bin. Different to previous techniques, the paper presents a method that uses multiple ranges bins to produce a spectrogram per range bin in order to represent the observed gesture over all four dimensions of time, Doppler, space and polarization. A comparison of the traditional and the newly proposed technique is shown and the improvements demonstrated are observed to be significant.
ID  - discovery10073829
AV  - public
KW  - Micro-Doppler
KW  -  Classification
KW  -  MachineLearning
KW  -  FMCW Radar
KW  -  Feature extraction
KW  -  Spectrogram
KW  -  Sensors
KW  -  Radar
KW  -  Databases
KW  -  Training
KW  -  Radio frequency
Y1  - 2019/09/16/
A1  - Ritchie, M
A1  - Jones, AM
UR  - https://doi.org/10.1109/RADAR.2019.8835782
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -