A fixed-wing unmanned aerial vehicle falling body motion horizontal attitude estimation method

By fusing data from MEMS inertial sensors and airspeed indicators, combined with maneuvering strategies and adaptive extended Kalman filtering, the dependence of traditional methods on attitude estimation of fixed-wing UAVs is solved, achieving accurate attitude estimation and wing take-off in free fall motion, thus improving the adaptability and accuracy of UAVs.

CN115031730BActive Publication Date: 2026-05-19NANJING UNIV OF AERONAUTICS & ASTRONAUTICS +1
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Patent Information

Application Number
CN202210593249.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-05-27
Publication Date
2026-05-19
Estimated Expiration
2042-05-27

AI Technical Summary

Technical Problem

Traditional methods of transferring initial alignment place high demands on the carrier of fixed-wing UAVs and make it difficult to accurately estimate horizontal attitude in the event of satellite denial, affecting the timing of wing start-up and engine activation of the UAV.

Method used

A data fusion method based on MEMS inertial sensors and airspeed indicators is adopted, combined with maneuvering strategies and improved adaptive extended Kalman filtering, to estimate the horizontal attitude of the UAV. Zero-bias estimation of gyroscope and accelerometer is achieved through under-fuselage orientation judgment and sensor data preprocessing, and attitude angle is estimated in real time using quaternions.

Benefits of technology

It achieves accurate horizontal attitude estimation of UAVs during free fall, reduces dependence on launch platforms, improves the accuracy and adaptability of attitude estimation, and enables accurate wing deployment and engine start-up in complex environments.

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Abstract

The application discloses a fixed-wing unmanned aerial vehicle falling body motion horizontal attitude estimation method, which comprises the following steps: step one, pre-processing the gyroscope and accelerometer data, converting the sensor data to the front-right-down body coordinate system and normalizing; step two, preliminarily judging the belly orientation according to the skyward accelerometer; step three, after starting the engine, using a maneuvering strategy to estimate the preliminary constant zero offset of the gyroscope and the accelerometer; and step four, according to the gyroscope and accelerometer data, using an improved adaptive extended Kalman filtering method based on quaternions to fuse the sensor data, further estimating the zero offset of the gyroscope and the accelerometer, and simultaneously obtaining the real-time estimation of the horizontal attitude angle. The application overcomes the shortcoming of the traditional transfer alignment method which has high requirements for the delivery carrier, can determine the opening time of the wings and the starting time of the engine during the delivery process of the fixed-wing unmanned aerial vehicle, and realizes the estimation task of the horizontal attitude and the online estimation of the sensor error.
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