Airspeed-Aided State Estimation Algorithm of Small Fixed-Wing UAVs in GNSS-Denied Environments.

Area of Science:

• Aerospace Engineering
• Robotics and Control Systems
• Navigation and Guidance

Background:

• Global Navigation Satellite System (GNSS)-denied environments pose significant challenges for Unmanned Aerial Vehicle (UAV) navigation.
• Accurate estimation of attitude, velocity, and position is critical for UAVs, especially during dynamic flight maneuvers.

Purpose of the Study:

• To develop and validate an algorithm for improving the navigation accuracy of fixed-wing UAVs in GNSS-denied environments.
• To enhance the real-time estimation of non-gravitational acceleration, attitude, velocity, and height for UAVs undergoing dynamic flight.

Main Methods:

• Proposed a non-gravitational acceleration estimation algorithm using airspeed and Inertial Measurement Unit (IMU) sensors with a differential tracker (TD) model.
• Established a mapping between non-gravitational acceleration and attitude misalignment, compensated using a nonlinear complementary filtering model.
• Implemented a lightweight complementary filter for velocity estimation and fused barometer data for height and lift rate tracking.

Main Results:

• Demonstrated significant improvement in attitude estimation precision for dynamic flight conditions.
• Achieved horizontal position errors of less than 30m within 30s and 50m within 90s of flight.
• Obtained an average height channel error of 0.5m, indicating accurate height and lift rate tracking.

Conclusions:

• The developed algorithm provides accurate attitude, speed, and position calculations for UAVs in maneuvering environments.
• The algorithm's effectiveness was validated through real flight data comparisons with existing methods (ACF, EKF, NCF).
• The approach is suitable for deployment on low-cost fixed-wing UAVs, enhancing their operational capabilities in challenging navigation scenarios.