A Machine Learning-Based Method and System for Evaluating the Wind Resistance Performance of Small and Medium-Sized Unmanned Aerial Vehicles

CN121052118BActive Publication Date: 2026-05-26JIANGSU SUPERVISION & INSPECTION INST FOR PROD QUALITY +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JIANGSU SUPERVISION & INSPECTION INST FOR PROD QUALITY
Filing Date
2025-08-15
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In existing technologies, traditional methods for characterizing the dynamic characteristics of UAVs suffer from nonlinearity issues due to Euler angle representation, leading to gimbal lock problems during large-angle maneuvers. Traditional methods also fail to adequately explore the spatiotemporal correlations of sensor data and lack effective physical constraint mechanisms, resulting in insufficient accuracy and reliability in assessments under complex wind field conditions.

Method used

Data is acquired using a nine-axis inertial measurement unit, a global positioning module, and a differential barometer. Noise is eliminated by sliding window frame processing and Kalman filtering. The least squares method is used to fit the trajectory to extract time-domain and frequency-domain features, which are then converted into Lie group spatial features. These features are evaluated using a lightweight neural network model, and the model is updated through a federated learning mechanism. The control strategy is dynamically switched to improve the evaluation accuracy and reliability.

Benefits of technology

It significantly improves the accuracy of wind resistance performance assessment and control reliability of small and medium-sized UAVs under complex wind field conditions, and solves the kinematic error problem introduced by Euler angle representation in traditional methods.

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Abstract

This invention discloses a machine learning-based method and system for evaluating the wind resistance performance of small and medium-sized unmanned aerial vehicles (UAVs), relating to the field of UAV flight control technology. The method includes: acquiring raw sensor data using a nine-axis inertial measurement unit (IMU), a global positioning module (GPS), and a differential barometer; performing sliding window framing processing on the raw sensor data; eliminating impulse noise in angular velocity and linear acceleration using median filtering; smoothing the barometric pressure data using Kalman filtering; fitting the GPS trajectory using the least squares method and extracting time-domain and frequency-domain features to generate a time-series feature matrix; and converting the Euler angle features in the time-series feature matrix into a Lie group space. This invention effectively solves the kinematic error problem of traditional methods under large-angle maneuvers by using feature representations constrained by rigid body kinematics and a physics-guided machine learning framework, significantly improving the evaluation accuracy and control reliability under complex wind field conditions.
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