  Commercial radar sensing is gaining relevance and machine learning algorithms
constitute one of the key components that are enabling the spread of this radio
technology into areas like surveillance or healthcare. However, radar datasets
are still scarce and generalization cannot be yet achieved for all radar
systems, environment conditions or design parameters. A certain degree of fine
tuning is, therefore, usually required to deploy machine-learning-enabled radar
applications. In this work, we consider the problem of unsupervised domain
adaptation across radar configurations in the context of deep-learning human
activity classification using frequency-modulated continuous-wave. For that, we
focus on the theory-inspired technique of Margin Disparity Discrepancy, which
has already been proved successful in the area of computer vision. Our
experiments extend this technique to radar data, achieving a comparable
accuracy to fewshot supervised approaches for the same classification problem.
