Angle of Attack Synthesis during Aircraft Landing Phase Using BP Neural Network: A Data-Driven Approach Based on Multi-Sensor Fusion

Angle of attack (AoA) is a crucial parameter for flight safety, particularly during the landing phase of civil aircraft. Traditional measurement methods face challenges such as sensor limitations and environmental interference. This paper presents a novel data-driven approach using a BP neural network to synthesize AoA by fusing multiple flight parameters, including airspeed, ground speed, wind speed, and heading angle. The proposed method demonstrates high accuracy and reliability, offering a promising solution to enhance flight safety during landing.