UAV Wind Speed Estimation Using Flight Data and System Identification
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Solution Overview
Problem
Current wind velocity detection methods for UAVs require additional sensors and databases, increasing production costs and computational burdens, and affecting real-time performance.
Innovation Solution
A wind velocity measurement method that uses system identification based on flight data and attribute data to calculate wind velocity without a database or additional sensors, by determining the equivalent wind resistance coefficient and windward area using nonlinear functions and air density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wind velocity is measured by directly measuring air velocity using a wind velocity sensor, then wind velocity detection accuracy is improved, but production cost increases due to additional sensors
Solution Approach 1:
The UAV uses its own existing sensors (accelerometer, gyroscope, air speed sensor, altitude sensor) to measure wind velocity by detecting flight state changes, rather than requiring additional dedicated wind sensors. The flight control system processes data from these existing components to calculate wind velocity, making the system self-sufficient
Solution Approach 2:
Existing sensors on the UAV are made multi-functional by using them not only for basic flight control but also for wind velocity measurement. The accelerometer serves both for attitude control and wind detection, the air speed sensor provides data for both propulsion control and wind velocity calculation
2Measurement precision
If wind velocity is estimated by establishing a database or using big data calculation, then wind velocity estimation capability is improved, but computational burden and memory consumption increase
Solution Approach 1:
The patent replaces database lookup and big data computational methods with a physical model-based calculation approach. Instead of storing and querying large databases, the system uses mathematical models that relate flight state parameters to wind velocity, performing real-time calculations based on current sensor readings
Solution Approach 2:
The system changes the approach from using static database parameters to using dynamic flight state parameters. By monitoring real-time changes in acceleration, velocity, attitude, and altitude, the system calculates wind velocity through parameter differentiation and mathematical relationships rather than database retrieval
3Measurement precision
If database is loaded on the UAV for wind velocity estimation, then wind velocity detection capability is improved, but memory occupation and real-time performance deteriorate
Solution Approach 1:
The patent extracts the essential wind velocity measurement function from complex database systems and implements it through simplified real-time calculations. By taking out only the necessary computational logic and implementing it through direct mathematical relationships between flight parameters and wind velocity, the system achieves real-time performance without database overhead
Solution Approach 2:
The system performs preliminary actions by continuously monitoring flight state parameters and maintaining readiness to calculate wind velocity at any moment. The flight control system is already processing acceleration, velocity, and attitude data for flight control purposes, so wind velocity calculation can be performed immediately using these pre-collected parameters
Data Source
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AI summary
The present invention relates to a wind velocity measurement method, a wind velocity estimator and an unmanned aerial vehicle (UAV). The wind velocity measurement method includes: determining current wind resistance interference of a UAV by means of system identification based on flight data and attribute data of the UAV; and calculating a wind velocity of a flight environment of the UAV according to the wind resistance interference and the inherent wind resistance of the UAV. The method realizes the wind velocity measurement by identifying parameters based on the principle of system identification without a newly added wind velocity sensor and an external database. Therefore, not only hardware device costs are saved, but also an additional computing burden and a problem about real-time performance are avoided. The method is simple and requires low costs.