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An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm.

Zeyang Wen1, Gongliu Yang1, Qingzhong Cai1

  • 1School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

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|August 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to calibrate inertial measurement unit (IMU) biases for strapdown inertial navigation systems (SINS). The KF-based AdaGrad algorithm improves accuracy when IMU sensor noise is irregular, enhancing navigation performance.

Keywords:
Kalman filtergradient descentinertial measurement unit (IMU) calibrationstrapdown inertial navigation system (SINS)

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Area of Science:

  • Navigation Systems
  • Signal Processing
  • Sensor Calibration

Background:

  • Strapdown inertial navigation systems (SINS) rely on inertial measurement units (IMUs) for accurate positioning.
  • IMU biases significantly degrade SINS accuracy, especially with non-Gaussian sensor noise.
  • Traditional Kalman filtering (KF) assumes Gaussian white noise, limiting its effectiveness for irregular IMU sensor noise.

Purpose of the Study:

  • To develop a novel method for calibrating IMU biases in SINS.
  • To address the limitations of traditional Kalman filtering with irregular sensor noise.
  • To enhance the accuracy of IMU bias estimation.

Main Methods:

  • Proposed a KF-based AdaGrad algorithm for IMU bias calibration.
  • Utilized the adaptive subgradient method (AdaGrad) to manage step size.
  • Derived a KF-based AdaGrad numerical function and calibration algorithm.

Main Results:

  • The proposed KF-based AdaGrad method effectively improves IMU bias accuracy.
  • Successful validation in both static and car-mounted field tests.
  • Demonstrated superior performance compared to traditional methods for irregular noise.

Conclusions:

  • The KF-based AdaGrad algorithm offers a robust solution for IMU bias calibration.
  • This method enhances the reliability and accuracy of SINS.
  • Applicable to real-world navigation scenarios with challenging sensor noise characteristics.