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A Sequential Multiplicative Extended Kalman Filter for Attitude Estimation Using Vector Observations.

Fangjun Qin1, Lubin Chang2, Sai Jiang3

  • 1Department of Navigation Engineering, Naval University of Engineering, Wuhan 430000, China. haig2005@126.com.

Sensors (Basel, Switzerland)
|May 13, 2018
PubMed
Summary
This summary is machine-generated.

A new sequential multiplicative extended Kalman filter (SMEKF) improves attitude estimation accuracy using vector observations. This method offers more consistent performance across various initial errors compared to existing filters.

Keywords:
attitude estimationmultiplicative extended Kalman filtersequential estimation

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Robotics

Background:

  • Accurate attitude estimation is crucial for autonomous systems.
  • Existing methods like the multiplicative extended Kalman filter (MEKF) have limitations in sequential processing.
  • Vector observations are commonly used for attitude determination.

Purpose of the Study:

  • To propose a novel sequential multiplicative extended Kalman filter (SMEKF) for attitude estimation.
  • To enhance accuracy and consistency in attitude estimation using sequential vector observations.
  • To address limitations of existing MEKF variations and traditional sequential EKF.

Main Methods:

  • Sequential processing of vector observations to update attitude estimates.
  • Linearization of the measurement model for each sequential observation.
  • Delayed covariance update after all vector observations are processed, accounting for attitude reset operations.
  • Comparison with Murrell's MEKF and traditional sequential EKF through numerical simulations.

Main Results:

  • The proposed SMEKF demonstrates more consistent and accurate performance than MEKF and traditional sequential EKF.
  • The filter shows improved results across a wide range of initial estimate errors.
  • Sequential attitude updates lead to more accurate measurement model linearization.

Conclusions:

  • The SMEKF offers a superior approach for attitude estimation using sequential vector observations.
  • The method's design effectively handles the reset operation characteristic of attitude updates.
  • SMEKF provides a robust and accurate solution for attitude estimation challenges in dynamic systems.