  Generative Adversarial Networks (GANs) are considered the state-of-the-art in
the field of image generation. They learn the joint distribution of the
training data and attempt to generate new data samples in high dimensional
space following the same distribution as the input. Recent improvements in GANs
opened the field to many other computer vision applications based on improving
and changing the characteristics of the input image to follow some given
training requirements. In this paper, we propose a novel technique for the
denoising and reconstruction of the micro-Doppler ($\boldsymbol{\mu}$-D)
spectra of walking humans based on GANs. Two sets of experiments were collected
on 22 subjects walking on a treadmill at an intermediate velocity using a
\unit[25]{GHz} CW radar. In one set, a clean $\boldsymbol{\mu}$-D spectrum is
collected for each subject by placing the radar at a close distance to the
subject. In the other set, variations are introduced in the experiment setup to
introduce different noise and clutter effects on the spectrum by changing the
distance and placing reflective objects between the radar and the target.
Synthetic paired noisy and noise-free spectra were used for training, while
validation was carried out on the real noisy measured data. Finally,
qualitative and quantitative comparison with other classical radar denoising
approaches in the literature demonstrated the proposed GANs framework is better
and more robust to different noise levels.
