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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Adaptive State-Separated UFIR Filter for Attitude Estimation Using MARG Sensors.

Zepeng Li1, Yuhang Zhu1, Zheng Zhou1

  • 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.

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|February 27, 2026
PubMed
Summary

This study introduces an adaptive state-separated Unbiased Finite Impulse Response (UFIR) filter for improved vehicle attitude estimation. The novel algorithm optimizes estimation horizons for different states, enhancing accuracy and efficiency in engineering applications.

Keywords:
adaptiveattitude estimationmagnetic, angular rate, and gravity (MARG)quaternionsunbiased finite impulse response (UFIR) filter

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Area of Science:

  • Engineering
  • Control Systems
  • Signal Processing

Background:

  • Unbiased Finite Impulse Response (UFIR) filters are crucial for vehicle attitude estimation, offering robustness to initial conditions and noise.
  • However, UFIR filter performance is sensitive to the estimation horizon (N), with varying state correlations impacting accuracy.

Purpose of the Study:

  • To propose an adaptive state-separated UFIR (ASSUFIR) filtering algorithm for enhanced vehicle attitude estimation.
  • To address the challenge of optimal estimation horizon selection for different system states.
  • To improve computational efficiency compared to traditional UFIR filters.

Main Methods:

  • Developed an ASSUFIR algorithm utilizing quaternion properties to link attitude angles and reduce computational load.
  • Implemented QR decomposition to maintain computational efficiency despite structural modifications to the UFIR filter.
  • Validated the algorithm with simulated data and compared its performance against classical methods using real vehicle data.

Main Results:

  • The ASSUFIR algorithm successfully reduced computational burden by allowing state-specific estimation horizons.
  • Quaternion-based approach effectively leveraged attitude angle relationships.
  • Experimental results confirmed the practical applicability and effectiveness of the proposed ASSUFIR filter.

Conclusions:

  • The ASSUFIR algorithm offers a significant improvement for vehicle attitude estimation by adaptively managing estimation horizons.
  • The method enhances accuracy and computational efficiency, making it suitable for real-world engineering applications.
  • This approach provides a robust solution for complex dynamic systems requiring precise attitude estimation.