UAV Flight Altitude Measurement Based on AWA-AEIF Dual-Layer Information Fusion Algorithm.

Area of Science:

• Aerospace Engineering
• Robotics
• Geophysics

Background:

• Accurate altitude estimation is crucial for Unmanned Aerial Vehicle (UAV) navigation and safety.
• Barometric altimetry is prone to errors from atmospheric drift and dynamic disturbances, limiting UAV performance.
• Existing methods often struggle with real-time accuracy and dynamic response.

Purpose of the Study:

• To develop a robust, real-time differential barometric altimetry framework for UAVs.
• To enhance altitude estimation accuracy and reliability under dynamic flight conditions.
• To mitigate atmospheric variations and improve UAV operational capabilities.

Main Methods:

• Implementation of a dual-layer framework with a ground reference station and an onboard fusion scheme.
• Utilizing Adaptive Weighted Averaging (AWA) and an Adaptive Extended Information Filter (AEIF) for sensor fusion.
• Incorporating a physical pressure-height model and adaptive noise estimation within the AEIF.

Main Results:

• The proposed framework demonstrated decimeter-level altitude measurement accuracy in real flight tests.
• Achieved a significant reduction in altitude Root Mean Square Error (RMSE) from 4.05 m to 0.31 m over a two-hour flight.
• Showcased an order-of-magnitude improvement in altitude estimation accuracy compared to traditional barometric systems.

Conclusions:

• The dual-layer differential barometric altimetry framework provides a substantial advancement in UAV altitude estimation.
• The integration of a ground reference station and advanced onboard filtering effectively addresses atmospheric drift and dynamic disturbances.
• The validated decimeter-level accuracy enables more reliable and precise UAV operations in diverse environments.