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An improved self-alignment method for strapdown inertial navigation system based on gravitational apparent motion and

Xixiang Liu1, Yu Zhao1, Xianjun Liu1

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This study introduces a new self-alignment method for Strapdown Inertial Navigation Systems (SINS). The method effectively identifies gravitational apparent motion and avoids vector collinearity, improving navigation accuracy.

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Area of Science:

  • Navigation Systems Engineering
  • Geophysics
  • Signal Processing

Background:

  • Strapdown Inertial Navigation Systems (SINS) require accurate initial alignment.
  • Self-alignment methods using gravitational apparent motion face challenges with noise and vector collinearity.

Purpose of the Study:

  • To develop a robust self-alignment method for SINS.
  • To address the identification of apparent motion in noisy accelerometer data.
  • To overcome the dual-vector collinearity problem in alignment solutions.

Main Methods:

  • A parameter identification and reconstruction algorithm is proposed.
  • The algorithm effectively identifies apparent motion from noisy accelerometer measurements.
  • A dual-vector reconstruction algorithm is designed to avoid collinearity.

Main Results:

  • The proposed algorithm successfully identifies apparent motion from accelerometer data.
  • The reconstruction algorithm effectively avoids the collinear problem.
  • Simulations and turntable tests confirm self-alignment in swinging conditions.

Conclusions:

  • The novel self-alignment method enhances SINS performance.
  • Achieved alignment accuracy meets theoretical sensor precision limits.
  • The method is effective even under dynamic swinging conditions.