A New Bias Error Prediction Model for High-Precision Transfer Alignment.

Area of Science:

• Navigation Systems
• Mechanical Engineering
• Signal Processing

Background:

• Acceleration-based transfer alignment is crucial for high-accuracy navigation.
• Bias errors can arise from complex dynamic interactions, such as cross-correlation between lever-arm and ship motion.
• Existing models may not fully capture these dynamic error sources.

Purpose of the Study:

• To investigate the bias error in acceleration-based transfer alignment for ships.
• To identify the cross-correlation between dynamic lever-arm and linear ship motion as a primary cause of bias error.
• To develop a novel prediction model for this specific bias error.

Main Methods:

• Utilized a Bernoulli-Euler Beam model to simulate ship hull-girder dynamics and verify cross-correlation.
• Applied ordinary least squares theory to derive the mathematical mechanism and prediction model for bias error.
• Conducted simulation experiments to analyze the influence of dynamic lever arm acceleration, ship motion acceleration, and their cross-correlation.

Main Results:

• Confirmed the existence of cross-correlation between dynamic lever-arm and ship motion using the beam model.
• The developed bias error prediction model aligns well with simulation experiment outcomes.
• Identified amplitude of dynamic lever arm acceleration, ship motion acceleration, and their cross-correlation as key influencing factors.

Conclusions:

• The proposed prediction model effectively captures the bias error mechanism in transfer alignment.
• This model shows potential for enhancing the accuracy of high-precision ship transfer alignment systems.
• Understanding and mitigating cross-correlation errors is vital for robust navigation solutions.