A MIMO Radar-based Few-Shot Learning Approach for Human-ID

Radar for deep learning-based human identification has become a research area of increasing interest. It has been shown that micro-Doppler
$(\mu-\mathbf{D})$
can reflect the walking behavior, through capturing the periodic limbs micro-motions. One of the main aspects is maximizing the number of included classes, while considering the real-time and training dataset size constraints. In this paper, a multiple-input-multiple-output (MIMO) radar is used to formulate micro-motion spectrograms of the elevation angular velocity
$(\mu-\omega)$
. The effectiveness of concatenating this newly-formulated spectrogram with the commonly used
$\mu-\mathrm{D}$
ones is investigated. To accommodate for non-constrained real walking motion, an adaptive cycle segmentation framework is utilized and a metric learning network is trained on half gait cycles
$(\approx 0.5\mathrm{s})$
. Studies on the effects of various numbers of classes (5–20), different dataset sizes, and varying observation time windows (1–2 s) are conducted. A non-constrained walking dataset of 22 subjects is collected with different aspect angles with respect to the radar. The proposed few-shot learning (FSL) approach achieves a classification error of 11.3 % with only 2 min of training data per subject.