  This paper considers human activity classification for an indoor radar
system. Human motions generate nonstationary radar returns which represent
Doppler and micro-Doppler signals. The time-frequency (TF) analysis of
micro-Doppler signals can discern subtle variations on the motion by precisely
revealing velocity components of various moving body parts. We consider radar
for activity monitoring using TF-based machine learning approach exploiting
both temporal and spatial degrees of freedom. The proposed approach captures
different human motion representations more vividly in joint-variable data
domains achieved through beamforming at the receiver. The radar data is
collected using real time measurements at 77 GHz using four receive antennas,
and subsequently micro-Doppler signatures are analyzed through machine learning
algorithm for classifications of human walking motions. We present the
performance of the proposed multi antenna approach in separating and
classifying two closely walking persons moving in opposite directions.
