Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and...

Area of Science:

• Navigation Systems Engineering
• Sensor Fusion
• Estimation Theory

Background:

• Inertial Navigation Systems (INS) are crucial for navigation but suffer from drift.
• Integrating Doppler Velocity Logs (DVL) and gyrocompasses (IDG) improves INS accuracy.
• Accurate initial bias estimation is critical for long-term INS/DVL and IDG performance.

Purpose of the Study:

• To propose and evaluate a novel initial bias estimation method using a trajectory generator (TG).
• To compare the TG method against the conventional Kalman Filter (KF) for INS/DVL and IDG systems.
• To assess the effectiveness of the TG method for micro-electro-mechanical system (MEMS) sensors on vessels.

Main Methods:

• Development of a trajectory generator (TG) for initial bias estimation.
• Implementation and testing of the TG method with INS/DVL and INS/DVL/gyrocompass (IDG) configurations.
• Comparative analysis against the conventional Kalman Filter (KF) estimation method.

Main Results:

• The TG method achieved 100% probability of horizontal position error < 1 nautical mile within 400s bias interval, compared to 9% for KF.
• Average 1-hour horizontal position errors for IDG were 493m (TG) vs. 507m (KF).
• Variation in position error was significantly reduced with TG (2m) compared to KF (27m).

Conclusions:

• The proposed trajectory generator method is highly effective for initial bias estimation in INS/DVL and IDG systems.
• The TG method offers superior accuracy and stability over the conventional KF, especially for MEMS sensors on moving platforms.
• This novel approach enhances the reliability and precision of navigation systems in dynamic marine environments.