Wind-Corrected Payload Assessment of a Hovering Drone via Micro-Doppler Branch Separation

Abstract

Remote monitoring of drones is important for airspace management and security, including assessment of unauthorized payloads. Prior radar studies have established that payload state can affect drone signatures, but wind can alter rotor speeds and confound a payload estimate. Here, we examine a hovering DJI Mini 2 under controlled anechoic-chamber and wind-tunnel conditions. A balanced, top-mounted payload shifts the dominant blade-related micro-Doppler peak, whereas axial wind produces two spectral branches. We interpret the splitting as consistent with controller-mediated body tilt and thrust redistribution; rotor speed and attitude were not independently measured. At a 7 GHz carrier, increasing payload from 0 to 120 g shifts the dominant peak from approximately 340 to 430 Hz, giving a descriptive sensitivity of about 0.75 Hz/g for this platform. In the wind tunnel, the branch separation follows Δf = (16 Hz/(m/s))v, is the axial wind speed in m/s, and reaches approximately 52.8 Hz at 3.3 m/s while remaining nearly independent of the tested payload. The separation can therefore provide a model-specific wind estimate before payload correction. The resulting relations are presented as a platform-specific empirical calibration under controlled hovering. The specific contribution is the observation and empirical modeling of wind-induced branch splitting, which complements prior payload-classification methods.