  Studying animal movements is essential for effective wildlife conservation
and conflict mitigation. For aerial movements, operational weather radars have
become an indispensable data source in this respect. However, partial
measurements, incomplete spatial coverage, and poor understanding of animal
behaviours make it difficult to reconstruct complete spatio-temporal movement
patterns from available radar data. We tackle this inverse problem by learning
a mapping from high-dimensional radar measurements to low-dimensional latent
representations using a convolutional encoder. Under the assumption that the
latent system dynamics are well approximated by a locally linear Gaussian
transition model, we perform efficient posterior estimation using the classical
Kalman smoother. A convolutional decoder maps the inferred latent system states
back to the physical space in which the known radar observation model can be
applied, enabling fully unsupervised training. To encourage physical
consistency, we additionally introduce a physics-informed loss term that
leverages known mass conservation constraints. Our experiments on synthetic
radar data show promising results in terms of reconstruction quality and
data-efficiency.
