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Strong tracking dimensionality-reduction CKF algorithm for SINS initial alignment under large misalignment angle.

Yong Yang1,2, Donglin Li3,4, Yujie Fu3,4

  • 1Department of Flight Technology, Civil Aviation Flight University of China, Guanghan, 618307, China. yangyong@cafuc.edu.cn.

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Summary
This summary is machine-generated.

A new Strong Tracking Dimensionality-Reduction Cubature Kalman Filter (STDR-CKF) algorithm improves Strapdown Inertial Navigation System (SINS) initial alignment. It reduces computational load and odometer errors for faster, more accurate navigation.

Keywords:
Cubature kalman filterInitial alignmentStrapdown inertial navigation systemStrong tracking filter

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Area of Science:

  • Navigation Systems Engineering
  • Control Theory
  • Signal Processing

Background:

  • Accurate initial alignment is crucial for Strapdown Inertial Navigation System (SINS) operation.
  • Kalman filtering and evolutionary algorithms are common but computationally intensive methods for SINS initial alignment.
  • Large azimuth misalignment angles and odometer errors pose significant challenges to traditional methods.

Purpose of the Study:

  • To develop a computationally efficient and robust algorithm for SINS initial alignment, particularly under large azimuth misalignment.
  • To address and mitigate odometer increment errors caused by pulse count truncation.
  • To enhance the filtering stability and accuracy of the SINS initial alignment process.

Main Methods:

  • Proposed a Strong Tracking Dimensionality-Reduction Cubature Kalman Filter (STDR-CKF) algorithm.
  • Implemented a state variable separation framework (6 nonlinear, 12 linear states) to reduce computational load.
  • Utilized differential calculations between SINS and odometer data for feedback correction to suppress odometer errors.
  • Integrated a Strong Tracking Filter (STF) diminishing factor for real-time dynamic feedback correction and improved robustness.

Main Results:

  • The STDR-CKF algorithm significantly reduces the computational load compared to the conventional Cubature Kalman Filter (CKF).
  • Odometer increment errors are effectively suppressed through the proposed differential calculation and feedback correction method.
  • Experimental results show enhanced filtering stability and higher accuracy under large azimuth misalignment conditions.
  • The proposed method achieves a shorter initial alignment time compared to conventional algorithms.

Conclusions:

  • The STDR-CKF algorithm offers a superior solution for SINS initial alignment, balancing computational efficiency with high accuracy.
  • The method effectively overcomes limitations of traditional approaches, particularly in scenarios with large azimuth misalignment and odometer inaccuracies.
  • This advancement contributes to more reliable and efficient navigation system performance.