  Micro-Doppler signatures contain considerable information about target
dynamics. However, the radar sensing systems are easily affected by noisy
surroundings, resulting in uninterpretable motion patterns on the micro-Doppler
spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and
interference. These issues lead to difficulty in, for example motion feature
extraction, activity classification using micro Doppler signatures ($\mu$-DS),
etc. In this paper, we propose a latent feature-wise mapping strategy, called
Feature Mapping Network (FMNet), to transform measured spectrograms so that
they more closely resemble the output from a simulation under the same
conditions. Based on measured spectrogram and the matched simulated data, our
framework contains three parts: an Encoder which is used to extract latent
representations/features, a Decoder outputs reconstructed spectrogram according
to the latent features, and a Discriminator minimizes the distance of latent
features of measured and simulated data. We demonstrate the FMNet with six
activities data and two experimental scenarios, and final results show strong
enhanced patterns and can keep actual motion information to the greatest
extent. On the other hand, we also propose a novel idea which trains a
classifier with only simulated data and predicts new measured samples after
cleaning them up with the FMNet. From final classification results, we can see
significant improvements.
