Drone Carry-on Weight and Wind Flow Assessment via Micro-Doppler Analysis

2025-10-26

AI Summary

The coupled effects of payload variation and wind-induced disturbances on hovering UAV dynamics impede remote discrimination, limiting low-altitude non-cooperative target monitoring. To address this, we propose a decoupled monitoring method based on micro-Doppler spectral splitting features. Specifically, we exploit topological differences in the micro-Doppler spectrum—arising from payload-induced rigid-body deformation versus wind-induced attitude tilt—in rotor radar echoes, enabling, for the first time experimentally, simultaneous separation and quantitative estimation of both factors. A controlled experimental platform is established by integrating electromagnetic shielding with a wind tunnel, and pulsed radar sensing is combined with a branch-guided deterministic signal processing algorithm. Across multiple operational conditions, the method accurately retrieves payload mass as well as wind direction and speed. This approach significantly enhances remote, non-cooperative sensing accuracy and physical interpretability for small hovering UAVs.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Remote monitoring of drones has become a global objective due to emerging applications in national security and managing aerial delivery traffic. Despite their relatively small size, drones can carry significant payloads, which require monitoring, especially in cases of unauthorized transportation of dangerous goods. A drone's flight dynamics heavily depend on outdoor wind conditions and the carry-on weight, which affect the tilt angle of a drone's body and the rotation velocity of the blades. A surveillance radar can capture both effects, provided a sufficient signal-to-noise ratio for the received echoes and an adjusted postprocessing detection algorithm. Here, we conduct a systematic study to demonstrate that micro-Doppler analysis enables the disentanglement of the impacts of wind and weight on a hovering drone. The physics behind the effect is related to the flight controller, as the way the drone counteracts weight and wind differs. When the payload is balanced, it imposes an additional load symmetrically on all four rotors, causing them to rotate faster, thereby generating a blade-related micro-Doppler shift at a higher frequency. However, the impact of the wind is different. The wind attempts to displace the drone, and to counteract this, the drone tilts to the side. As a result, the forward and rear rotors rotate at different velocities to maintain the tilt angle of the drone body relative to the airflow direction. This causes the splitting in the micro-Doppler spectra. By performing a set of experiments in a controlled environment, specifically, an anechoic chamber for electromagnetic isolation and a wind tunnel for imposing deterministic wind conditions, we demonstrate that both wind and payload details can be extracted using a simple deterministic algorithm based on branching in the micro-Doppler spectra.

Problem

• Assessing drone carry-on weight and wind conditions through micro-Doppler analysis
• Disentangling wind and payload effects on drone flight dynamics
• Extracting wind and payload details from micro-Doppler spectral branching

Innovation

• Micro-Doppler analysis separates wind and weight effects
• Algorithm detects payload via symmetric rotor speed increase
• Wind tilt causes rotor speed asymmetry in spectra