ResAlignNet: A data-driven approach for INS/DVL alignment

Keyphrases

• Autonomous Underwater Vehicle 100%
• Data-driven Approach 100%
• Convergence Time 100%
• Motion Pattern 100%
• Inertial Navigation System 50%
• Doppler Velocity 50%
• In Situ 50%
• Sensor Measurement 50%
• Alignment Method 50%
• Learnability 50%
• Synthetic Data 50%
• Time Dependence 50%
• Experimental Validation 50%
• Operational Scenario 50%
• Alignment Problem 50%
• Satellite Signals 50%
• Alignment Accuracy 50%
• Vehicle Maneuver 50%
• Rapid Convergence 50%
• Acoustic Positioning System 50%
• Operational Flexibility 50%
• Deep Neural Network Optimization 50%
• Vehicle Deployment 50%
• Sim2real Knowledge Transfer 50%
• ResNet-18 Architecture 50%
• Complex Vehicles 50%
• Sensor Specifications 50%
• Onboard Sensors 50%
• 1D-ResNet 50%
• Precise Integration 50%
• Prescribed Motion 50%
• Accurate Alignment 50%
• Robust Sensor 50%
• Position-aided 50%
• Sensor-agnostic 50%
• Overreliance 50%
• Velocity-based Method 50%
• Underwater Navigation 50%
• Satellite Navigation 50%
• Scenario Definition 50%

Engineering

• Autonomous Underwater Vehicle 100%
• Convergence Time 100%
• Inertial navigation systems 100%
• Doppler Velocity 50%
• Limitations 50%
• Sensor Measurement 50%
• Transfer Learning 50%
• Reference Frame 50%
• Deep Neural Network 50%
• Acoustic Positioning System 50%
• Operational Scenario 50%
• Resnet 50%