1 research outputs found
Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network
This study proposes a personal identification technique that applies machine
learning with a two-layered convolutional neural network to spectrogram images
obtained from radar echoes of a target person in motion. The walking and
sitting motions of six participants were measured using an ultrawideband radar
system. Time-frequency analysis was applied to the radar signal to generate
spectrogram images containing the micro-Doppler components associated with limb
movements. A convolutional neural network was trained using the spectrogram
images with personal labels to achieve radar-based personal identification. The
personal identification accuracies were evaluated experimentally to demonstrate
the effectiveness of the proposed technique.Comment: 9 pages, 7 figures, and 3 table