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Attitude Algorithm of Gyroscope-Free Strapdown Inertial Navigation System Using Kalman Filter.

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This study introduces a novel gyroscope-free strapdown inertial navigation system (GFSINS) using accelerometers for precise attitude determination. The new Kalman filter-based algorithm significantly improves angular velocity calculation accuracy for navigation systems.

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Kalman filteringaccelerometer arrayangular velocity calculationattitude algorithm

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Area of Science:

  • Inertial Navigation Systems
  • Sensor Fusion
  • Attitude Determination

Background:

  • Traditional inertial navigation systems often rely on gyroscopes, which can be bulky and expensive.
  • Gyroscope-free strapdown inertial navigation systems (GFSINS) offer a potential alternative by utilizing accelerometers for attitude calculation.
  • Accurate angular velocity calculation is critical for GFSINS performance, but existing methods face challenges like sign misjudgment.

Purpose of the Study:

  • To propose and validate a novel angular velocity fusion algorithm for a twelve-accelerometer GFSINS.
  • To enhance the accuracy of angular velocity calculation by addressing sign misjudgment issues.
  • To achieve high-precision attitude determination for applications like laser scanning projection systems.

Main Methods:

  • Analysis of a twelve-accelerometer configuration for GFSINS.
  • Development of a Kalman filter-based algorithm for angular velocity fusion.
  • Implementation of a sliding window correction method to improve extraction algorithm accuracy.
  • Fusion of integral and improved extraction algorithm data for optimal angular velocity estimation.

Main Results:

  • The proposed algorithm significantly reduces angular velocity error (maximum value and standard deviation) by one order of magnitude compared to existing methods.
  • Experimental results show calculated attitude angles with an average difference of less than 0.5° compared to laser tracker measurements.
  • The achieved accuracy meets the stringent requirements for attitude measurement in laser scanning projection systems.

Conclusions:

  • The developed Kalman filter-based fusion algorithm effectively enhances angular velocity calculation accuracy in GFSINS.
  • The sliding window correction method successfully mitigates sign misjudgment issues.
  • This GFSINS approach provides a viable, high-accuracy solution for attitude determination in demanding applications.