A kalman attitude evaluation method for unmanned aerial vehicles

By fusing multi-source sensor data using the Kalman filter algorithm and adjusting noise parameters in real time, the problem of untimely and inaccurate attitude adjustment in UAV attitude control methods is solved, thereby improving the flight stability and anti-interference capability of UAVs in complex environments.

CN122363347APending Publication Date: 2026-07-10
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Filing Date
2025-06-26
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing drone flight attitude control methods cannot dynamically adjust according to real-time environmental changes and flight status, resulting in untimely and inaccurate attitude adjustments, which can easily lead to flight instability or even loss of control, and poor anti-interference capabilities.

Method used

The Kalman filter algorithm is used to fuse multi-source sensor data. Recursive optimal estimation is performed by constructing state equations and observation equations, and the noise parameters of the Kalman filter are adjusted in real time to improve the attitude estimation accuracy and anti-interference capability.

Benefits of technology

It enables real-time and accurate attitude estimation of UAVs in complex environments, improves flight stability and anti-interference capabilities, and ensures the safe and efficient operation of UAVs.

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Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology and discloses a Kalman attitude evaluation method for UAVs. Addressing the problems of low accuracy and poor real-time performance in attitude evaluation of UAVs under complex weather conditions and varying terrain, this method deploys multiple types of sensors (barometric pressure, angular velocity, acceleration, and attitude angle sensors) on the UAV to collect flight data in real time, which is then transmitted to a central processing unit (CPU). The CPU constructs a state-space model based on the Kalman filter algorithm, performs prediction and update fusion processing on the sensor data, and optimizes the evaluation results using a PID control algorithm, achieving real-time, high-precision attitude evaluation of the UAV. This method effectively improves the attitude perception capability and stability of UAVs in complex environments and can be widely applied in aerial surveying, material delivery, environmental monitoring, and other fields, providing reliable attitude data support for efficient and safe UAV operations.
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