@article{discovery10136070,
          volume = {60},
           month = {November},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           title = {FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram},
            year = {2021},
         journal = {IEEE Transactions on Geoscience and Remote Sensing},
        keywords = {Micro-Doppler Spectrogram, Adversarial Autoencoder, Variational Autoencoder, Feature Mapping, Passive
WiFi Radar, Deep Learning, Activity Classification},
          author = {Tang, C and Li, W and Vishwakarma, S and Shi, F and Julier, S and Chetty, K},
        abstract = {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 (u-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.},
             url = {https://doi.org/10.1109/TGRS.2021.3121211}
}