  Deep learning techniques are subject to increasing adoption for a wide range
of micro-Doppler applications, where predictions need to be made based on
time-frequency signal representations. Most, if not all, of the reported
applications focus on translating an existing deep learning framework to this
new domain with no adjustment made to the objective function. This practice
results in a missed opportunity to encourage the model to prioritize features
that are particularly relevant for micro-Doppler applications. Thus the paper
introduces a micro-Doppler coherence loss, minimized when the normalized power
of micro-Doppler oscillatory components between input and output is matched.
The experiments conducted on real data show that the application of the
introduced loss results in models more resilient to noise.
