GAM-Based Mooring Alignment for SINS Based on An Improved CEEMD Denoising Method.

Area of Science:

• Navigation Systems Engineering
• Signal Processing
• Data Analysis

Background:

• Strapdown Inertial Navigation Systems (SINS) face self-alignment challenges due to sensor noise.
• Gravitational Apparent Motion (GAM) is used for coarse alignment, but its accuracy is limited by noise.
• Accurate attitude matrix determination is crucial for SINS performance.

Purpose of the Study:

• To propose a novel adaptive filter for SINS self-alignment.
• To address the issue of sensor measurement noise affecting alignment accuracy and time.
• To enhance the denoising capability for accelerometer measurements.

Main Methods:

• A novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is developed.
• An l2-norm is used to calculate the similarity measure between Probability Density Functions (PDFs) of Intrinsic Mode Functions (IMFs) and the original signal for denoising.
• The proposed method is compared against PDF-based Empirical Mode Decomposition (EMD-PDF) and Finite Impulse Response (FIR) filters.

Main Results:

• The CEEMD-based adaptive filter effectively denoises accelerometer measurements.
• The proposed method demonstrates superior performance over conventional denoising techniques.
• Both simulation and experimental results confirm the effectiveness of the CEEMD approach.

Conclusions:

• The novel CEEMD-based adaptive filter significantly improves SINS self-alignment.
• The method offers reduced alignment time and enhanced alignment accuracy.
• This approach provides a robust solution for mitigating sensor noise in SINS.