From Radar Blips to Species-Level Aerial Monitoring: Physics-Based Simulation Enables Biological Insight from Micro-Doppler Data

Monitoring how birds, bats and insects move through the airspace is essential for biodiversity assessment and for understanding how different taxa use this aerial domain. Radar has long enabled continuous observations of animal movements, but the lack of species- or trait-specific information has limited its ecological value. Recent advances in FMCW radar provide detailed micro-Doppler spectrograms that capture wingbeat dynamics with high temporal resolution, creating new opportunities for taxonomic differentiation. Yet these signatures remain difficult to interpret, because they are not directly linked to known biological traits or species-specific reference data.

 We present a physics-based simulation and inference framework that links articulated animal models directly to radar observables. The simulator describes detailed wing and body kinematics, including stroke geometry, flapping dynamics and morphology, and propagates these motions through radar backscatter model to generate realistic micro-Doppler signatures. These synthetic radar returns provide the basis for training an inference module that learns to recover the underlying kinematic parameters from field spectrograms. The recovered parameters, when passed through the forward simulator, reconstruct radar signatures that closely match measured data, showing that physically grounded supervision enables inverse kinematics without manual labels and produces biologically interpretable descriptors of flight.

 We apply the framework to a vertical FMCW radar dataset capturing birds, bats and insects moving through a fixed airspace column. When run on these unlabeled field recordings, the model retrieves trait-based descriptors that expose systematic differences in flight behaviour across taxa and allow tracks to be organized into biologically meaningful groups. This demonstration shows that physically grounded interpretation can reveal structure that is otherwise inaccessible in raw micro-Doppler data, offering a practical basis for continuous, multi-taxon monitoring of aerial biodiversity. By linking radar physics to biological traits in a scalable, non-invasive way, the approach bridges the gap between radar physics and ecology for monitoring avian biodiversity and expanding the biological interpretability of radar observations.