Simulating and Analyzing Micro-Doppler Radar Signatures of Moving Quadcopters With Translational and Rotational Dynamics

Robust machine learning solutions for drone detection in radar data critically depend on access to accurate and diverse training datasets. Yet, the scarcity of realistic drone micro-Doppler signatures—especially those capturing dynamic, real-world maneuvers—limits algorithm performance and generalizability. Existing simulation approaches are often restricted to stationary or hovering conditions, failing to reflect the complex micro-Doppler effects induced by translational flight. This paper addresses this limitation by presenting a comprehensive simulation framework that incorporates both translational and rotational maneuvers in quadcopter dynamics. By systematically varying rotor speeds and flight trajectories, we generate motion aware micro-Doppler radar signatures that capture the interplay between translational motion, rotor-induced micro-motions, and their impact on spectral features such as centroid migration, spectral spread, harmonic content, and temporal variability. Detailed analysis reveals that translational maneuvers introduce significant frequency migration, spectral broadening, and non stationary range profiles—key characteristics absent in stationary models. The resulting signatures provide a structured and in terpretable basis for analyzing motion-dependent micro-Doppler behavior, supporting feature exploration and algorithm development for radar-based drone detection. By explicitly modeling maneuver-induced effects, this work bridges a critical gap in micro-Doppler simulation and lays the groundwork for future integration with experimentally validated data and learning based detection frameworks.