An Improved Strapdown Inertial Navigation System Initial Alignment Algorithm for Unmanned Vehicles.

Area of Science:

• Robotics and Control Systems
• Navigation and Guidance Technology
• Signal Processing

Background:

• Unmanned vehicles rely heavily on navigation systems for autonomous control.
• Strapdown Inertial Navigation Systems (SINS) are crucial due to their independence.
• Initial alignment is fundamental to SINS performance but is affected by nonlinearity and uncertainty.

Purpose of the Study:

• To propose an improved initial alignment algorithm for SINS.
• To enhance the accuracy and reliability of SINS initial alignment.
• To address system nonlinearity and uncertainty challenges in SINS.

Main Methods:

• Signal denoising using Empirical Mode Decomposition (EMD) and Extreme Learning Machine (ELM) for coarse alignment.
• Improved Robust Huber Cubarure Kalman Filter (RHCKF) for fine alignment to mitigate nonlinearity and uncertainty.
• Real-world testing to validate the proposed algorithm's effectiveness.

Main Results:

• Suppression of high-frequency noise in inertial sensor signals.
• Enhanced accuracy and reliability in SINS initial alignment.
• Demonstrated superiority of the proposed algorithm through practical tests.

Conclusions:

• The developed initial alignment algorithm significantly improves SINS performance.
• The combination of EMD-ELM denoising and RHCKF effectively handles system nonlinearity and uncertainty.
• The proposed method offers a robust solution for unmanned vehicle navigation.