Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy with a Temporal Convolutional Network

Keyphrases

• UAV 100%
• Analytical Redundancy 100%
• Deep Learning 100%
• Airspeed Estimation 100%
• Unscented Kalman Filter 66%
• Air Data System 66%
• Control Input 33%
• Air Speed 33%
• Vehicle Model 33%
• Sensor Failure 33%
• Estimation Method 33%
• State-of-the-art Techniques 33%
• Flow Angle 33%
• Flight Condition 33%
• Efficient Model 33%
• Redundancy Techniques 33%
• Existing State 33%
• Aircraft Model 33%
• Inertial Measurement Unit 33%
• Drag Coefficient 33%
• Backup System 33%
• GPS Signal 33%
• U-Net 33%
• Elevator Control 33%
• Real Flight Data 33%
• Bidirectional Long Short-term Memory (BiLSTM) 33%
• Novel Fusion 33%

Engineering

• Unmanned Aerial Vehicle 100%
• Kalman Filter 66%
• Dynamic Models 33%
• Control Input 33%
• Units of Measurement 33%
• Sensor Data 33%
• Flight Data 33%
• Conventional Method 33%
• Flight Condition 33%
• Efficient Model 33%
• State-of-the-Art Method 33%
• Complex Aircraft 33%
• Elevators 33%
• Drag Coefficient 33%
• Inertial Measurement 33%
• Long Short-Term Memory 33%