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Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model.

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Summary

This study enhances vehicle positioning accuracy in GPS-denied areas using a two-level inertial navigation system (INS) error model and wavelet denoising. The combined approach significantly boosts precision and stability, especially during motion changes.

Keywords:
DWTEKFINSUWBfusion positioning methodtwo-level error model

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Area of Science:

  • Navigation Systems Engineering
  • Signal Processing
  • Robotics

Background:

  • Inertial Navigation System (INS) accuracy is crucial for vehicle positioning, particularly in Global Navigation Satellite System (GNSS)-denied environments or sensor data loss.
  • Existing INS error compensation models require enhancement for improved precision and stability.

Purpose of the Study:

  • To propose a novel two-level error model for INS, integrating mechanism and propagation error models.
  • To develop an INS and ultra-wideband (UWB) fusion positioning method using the proposed error model and an Extended Kalman Filter (EKF).
  • To improve raw inertial measurement unit (IMU) data quality using wavelet shrinkage denoising (SURE-Shrink).

Main Methods:

  • Development of a two-level INS error compensation model.
  • Implementation of an INS and UWB fusion positioning algorithm utilizing the Extended Kalman Filter (EKF).
  • Application of the SURE-Shrink wavelet shrinkage method for IMU data prefiltering.

Main Results:

  • The SURE-Shrink wavelet denoising method improved positioning accuracy by 76.6%.
  • The two-level error model further enhanced positioning accuracy by an additional 84.3%.
  • The two-level error model demonstrated superior computational stability and reduced trajectory fluctuations during vehicle motion state changes.

Conclusions:

  • The proposed two-level error model, combined with wavelet denoising and UWB fusion, significantly enhances INS positioning accuracy and stability.
  • This methodology offers a robust solution for precise vehicle navigation in challenging GNSS-denied environments.