Multi-Source Confidence Assessment-Based Adaptive Calibration for Deep-Sea Manned Submersible Integrated Navigation

Area of Science:

• Marine Engineering
• Robotics and Control Systems
• Underwater Navigation

Background:

• Manned submersible navigation systems face reliability challenges in complex deep-sea environments.
• Accurate positioning is critical for mission success and safety in underwater operations.

Purpose of the Study:

• To develop an adaptive fusion navigation method for manned submersibles.
• To enhance navigation system reliability using multi-dimensional confidence assessment.

Main Methods:

• Proposed a four-dimensional evaluation framework for Ultra-Short Baseline (USBL) positioning systems.
• Incorporated signal quality, geometric precision, environmental attenuation, and data stability into the assessment.
• Implemented an adaptive weight adjustment mechanism based on real-time reliability assessment.

Main Results:

• Achieved an average position error of 1.15 m under harsh conditions with anomalies and data loss.
• Demonstrated a 53.1% improvement over conventional methods in position accuracy.
• Showcased a 58.9% enhancement in scenarios with jump point anomalies.

Conclusions:

• The proposed method significantly improves the navigation reliability of manned submersibles in complex underwater acoustic environments.
• The adaptive fusion navigation approach offers substantial engineering application value for deep-sea exploration.