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A Coarse Alignment Algorithm Based on Vector Reconstruction via Sage-Husa AKF for SINS on a Swaying Base.

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Summary

This study introduces a novel coarse alignment algorithm for strapdown inertial navigation systems (SINSs). The method enhances initial attitude determination speed and accuracy, even under challenging swaying-base conditions, by reconstructing observation vectors using an adaptive Kalman filter.

Keywords:
SINSSage–Husaadaptive Kalman filtercoarse alignmentvector reconstruction

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Area of Science:

  • Navigation Systems Engineering
  • Inertial Navigation
  • Signal Processing

Background:

  • Strapdown inertial navigation systems (SINSs) require rapid initial attitude determination.
  • Coarse alignment methods are crucial for enhancing SINS initialization speed without external aids.
  • Inertial sensor errors and external disturbances limit the accuracy of traditional coarse alignment observation vector models.

Purpose of the Study:

  • To develop an improved coarse alignment algorithm for SINSs.
  • To address the limitations of observation vector model inaccuracy in existing methods.
  • To enhance the speed and accuracy of initial attitude determination under swaying-base conditions.

Main Methods:

  • Established an apparent velocity vector observation model.
  • Designed a sliding-window vector integration algorithm to mitigate cumulative observation vector errors.
  • Implemented a vector reconstruction model utilizing the Sage-Husa adaptive Kalman filter (AKF) for self-alignment.

Main Results:

  • Simulations and turntable experiments validated the proposed algorithm.
  • The vector reconstruction approach significantly improved alignment accuracy.
  • The method demonstrated superior performance compared to existing coarse alignment techniques.

Conclusions:

  • The proposed Sage-Husa AKF-based vector reconstruction coarse alignment algorithm effectively enhances SINS initial attitude determination.
  • The method overcomes limitations associated with observation vector inaccuracy and external disturbances.
  • This approach offers a robust solution for rapid and accurate SINS alignment in dynamic environments.