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Optimization of Ring Laser Gyroscope Bias Compensation Algorithm in Variable Temperature Environment.

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Summary
This summary is machine-generated.

This study introduces a composite temperature calibration model to address bias drift in ring laser gyroscopes (RLGs) within strapdown inertial navigation systems (SINS). The new model significantly enhances RLG precision across various temperatures, improving system accuracy.

Keywords:
overlappiecewise least squares fittingring laser gyroscopetemperature compensation

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Area of Science:

  • Navigation Systems Engineering
  • Optical Gyroscopes
  • Thermal Analysis

Background:

  • Strapdown inertial navigation systems (SINS) rely on ring laser gyroscopes (RLGs) for accurate positioning.
  • RLG performance degrades due to bias drift caused by self-heating and ambient temperature fluctuations.
  • Understanding RLG bias temperature drift is crucial for maintaining high accuracy in dynamic environments.

Purpose of the Study:

  • To develop and validate a composite temperature calibration scheme for RLGs.
  • To improve the accuracy and reliability of RLGs in SINS under varying thermal conditions.
  • To provide a computationally efficient and flexible compensation method for RLG bias drift.

Main Methods:

  • A composite temperature model was developed, incorporating derivative terms and cross-multipliers.
  • Piecewise least squares fitting with defined overlap regions was employed.
  • The model was evaluated for its ability to compensate RLG bias trends and smooth output curves.

Main Results:

  • The composite temperature model effectively compensates for RLG bias trends across ambient temperatures.
  • The proposed method significantly improves fitting accuracy and smooths the gyroscope's output.
  • Precision improvements of 64.9%, 15.7%, and 3.6% were observed for laser gyros in a SINS.

Conclusions:

  • The composite temperature calibration scheme offers a viable solution for RLG bias drift.
  • The method demonstrates engineering applicability due to its computational efficiency and flexible design.
  • Enhanced RLG performance translates to improved overall SINS accuracy and reliability.