Adaptive GNSS/INS Integration Based on Supervised Machine Learning Approach

Keyphrases

• Adaptive Kalman Filter 100%
• Supervised Machine Learning 100%
• Multipath Effect 50%
• Urban Areas 50%
• Controller 25%
• Adaptation 25%
• Machine Learning 25%
• Fuzzy Logic 25%
• Principal Coordinate Analysis (PCoA) 25%
• Rapid Change 25%
• Learning Algorithm 25%
• Major Features 25%
• Process Noise Covariance 25%
• Classification Model 25%
• Multi-sensor 25%
• Operation Data 25%
• Labeled Data 25%
• Localization Error 25%
• Feature Data 25%
• Localization Accuracy 25%
• UAV 25%
• Kalman 25%
• Condition Identification 25%
• Accurate Localization 25%
• Random Forest 25%
• Localization Results 25%
• Fuzzy Logic Algorithm 25%
• Localization Performance 25%
• Decision Tree Forest 25%
• GNSS-based Localization 25%
• Random Forest Machine Learning 25%
• Allan Variance Analysis 25%
• Integration Performance 25%

Engineering

• Inertial Navigation System 100%
• Learning Approach 100%
• Kalman Filter 100%
• Learning System 100%
• Multipath Effect 50%
• Random Forest 50%
• Unmanned Aerial Vehicle 25%
• Localization Accuracy 25%
• Rapid Change 25%
• Process Noise 25%
• Major Feature 25%
• Localization Error 25%
• Common Method 25%
• Component Analysis 25%
• Localization Performance 25%
• Learning Algorithm 25%

Computer Science

• Machine Learning Approach 100%
• Kalman Filter 100%
• Machine Learning 75%
• Learning System 75%
• Random Decision Forest 50%
• Fuzzy Mathematics 50%
• Classification Models 25%
• Principle Component Analysis 25%
• Decision Tree 25%
• multi sensor 25%
• Learning Algorithm 25%
• Variance Analysis 25%
• Unmanned Aerial Vehicle 25%