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In-motion coarse alignment method based on reconstructed observation vectors.

Xiang Xu1, Xiaosu Xu1, Yiqing Yao1

  • 1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China.

The Review of Scientific Instruments
|April 5, 2017
PubMed
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This study introduces an improved in-motion coarse alignment method using reconstructed observation vectors. The novel approach enhances accuracy by filtering sensor noise and outliers, outperforming existing techniques.

Area of Science:

  • Navigation Systems
  • Signal Processing
  • Control Theory

Background:

  • Inertial sensors are crucial for navigation but susceptible to complex noises and outliers.
  • Existing in-motion alignment methods face challenges with sensor noise and gross outliers.
  • Accurate coarse alignment is essential for reliable navigation system performance.

Purpose of the Study:

  • To propose a novel in-motion coarse alignment method.
  • To enhance the robustness and accuracy of alignment algorithms.
  • To address challenges posed by sensor noise and outliers in inertial navigation.

Main Methods:

  • Reconstruction of observation vectors from inertial sensor outputs.
  • Application of Infinite Impulse Response (IIR) digital filtering to suppress high-frequency noise.

Related Experiment Videos

  • Design of a novel adaptive Kalman filter for parameter matrix recognition.
  • Implementation of Huber's M-estimation for robust outlier suppression.

    • Main Results:

    • The proposed method effectively suppresses high-frequency noise and gross outliers.
    • Simulation and field trials validate the method's effectiveness.
    • Demonstrated superior performance compared to conventional optimization-based and digital filter alignment methods.

    Conclusions:

    • The developed in-motion coarse alignment method offers enhanced accuracy and robustness.
    • The combination of IIR filtering, adaptive Kalman filtering, and robust estimation provides significant advantages.
    • This method represents a notable advancement in inertial navigation system alignment.