  We introduce a simple but effective technique in automatic hand gesture
recognition using radar. The proposed technique classifies hand gestures based
on the envelopes of their micro-Doppler signatures. These envelopes capture the
distinctions among different hand movements and their corresponding positive
and negative Doppler frequencies which are generated during each gesture act.
We detect the positive and negative envelopes separately, and form a feature
vector of their augmentation. We use the $k$-nearest neighbor ($k$NN)
classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance
(L2), so as not to diminish small but critical envelope values. It is shown
that this method outperforms both low-dimension representation techniques based
on principal component analysis (PCA) and sparse reconstruction using
Gaussian-windowed Fourier dictionary, and can achieve very high classification
rates.
