A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition

Keyphrases

• Performance Prediction 100%
• Machine Learning Based 100%
• Urban Air Mobility 100%
• Environment Recognition 100%
• GNSS Performance 100%
• Environmental Parameters 40%
• Urban Environment 20%
• Random Forest Algorithm 20%
• Least Squares Support Vector Regression (LSSVR) 20%
• Regression Forest 20%
• Synthetic Data 20%
• Neighborhood Support 20%
• Mission-critical 20%
• Prediction Algorithms 20%
• Performance Monitoring 20%
• Prediction Parameters 20%
• Air Cargo 20%
• Novel Machine 20%
• Parameter Importance 20%
• GNSS Simulator 20%
• Urban Canyon 20%
• K-nearest 20%
• Result Prediction 20%

Computer Science

• Urban Air Mobility 100%
• Machine Learning 100%
• Learning System 100%
• Random Decision Forest 50%
• Urban Environment 50%
• Support Vector Regression 50%
• Performance Monitoring 50%
• Synthetic Data 50%
• Primary Navigation 50%
• Prediction Performance 50%

Engineering

• Performance Prediction 100%
• Air Mobility 100%
• Learning System 100%
• Environmental Parameter 66%
• Random Forest 33%
• Prediction Performance 33%
• Suburban Environment 33%
• Support Vector Machine 33%
• Nearest Neighbor 33%