Dynamics-based realistic simulator for rotation rate estimation of quadcopter drones with multistatic radar

Abstract In this paper, we introduce and validate signal processing techniques for the estimation of the individual rotation rates of multicopter’s Unmanned Aerial Vehicle (UAV), by exploiting a multistatic radar echoes. To validate the techniques, which have been introduced in our previous works, in this paper, we present a simulator for the multistatic radar echoes scattered by a UAV that integrates quadcopter’s aerodynamics with electromagnetic modeling to generate realistic radar return, characterized by blades rotating in different directions and with different rates depending on the flight trajectory to be traveled. This simulator enables the validation of signal processing. We leverage the simulator to assess the effectiveness of autocorrelation and cross-correlation (XCF) techniques in separating multiple propellers, both in hovering and along a realistic flight trajectory. Simulated results confirm that XCF allows distinguishing counter-rotating propellers, while co-rotating ones remain unresolved due to their similar speeds. The simulator also demonstrates how variations in rotation rates can be used to infer the presence and weight of a payload. Experimental validation with a C-band continuous wave radar confirms the findings and highlights the impact of material properties on resolution. Finally, we exploit the simulator to investigate the effect of higher carrier frequencies, showing that increasing the operating frequency improves the ability to discriminate co-rotating propellers, supporting improved UAV classification, payload estimation, and trajectory prediction for anti-drone applications.