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Updated: Oct 5, 2025

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MEMS Accelerometer Noises Analysis Based on Triple Estimation Fractional Order Algorithm.

Michal Macias1, Dominik Sierociuk1, Wiktor Malesza1

  • 1Institute of Control and Industrial Electronics, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland.

Sensors (Basel, Switzerland)
|January 22, 2022
PubMed
Summary

This study identifies fractional order noise parameters in MEMS accelerometer data using the Triple Estimation algorithm. Findings confirm fractional noise existence, crucial for advanced filtering in inertial navigation systems.

Keywords:
estimation of fractional order systemsfractional Kalman filterfractional calculusfractional order noise

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Area of Science:

  • Signal Processing
  • Control Systems Engineering
  • Sensor Technology

Background:

  • Micro-Electro-Mechanical Systems (MEMS) accelerometers are vital for inertial navigation.
  • Understanding noise characteristics in MEMS sensors is critical for accurate data.
  • Fractional order dynamics offer a more nuanced model for complex noise phenomena.

Purpose of the Study:

  • To identify parameters of fractional order noises.
  • To apply these methods to noise data from MEMS accelerometers.
  • To evaluate the effectiveness of the Triple Estimation algorithm for fractional noise analysis.

Main Methods:

  • Utilized the Triple Estimation algorithm for simultaneous state, fractional order, and parameter estimation.
  • Performed numerical analyses on fractional constant and variable order systems with Gaussian noise.
  • Analyzed experimental data from a SparkFun MPU9250 Inertial Measurement Unit (IMU) accelerometer.

Main Results:

  • Confirmed the capability of the Triple Estimation algorithm in estimating fractional noise parameters.
  • Demonstrated the existence of fractional noise in MEMS accelerometer data across x, y, and z axes.
  • Numerical simulations validated the algorithm's performance on fractional systems.

Conclusions:

  • Fractional noise is present in MEMS accelerometer measurements.
  • The Triple Estimation algorithm is effective for identifying fractional noise parameters.
  • This characterization is essential for developing improved filtering algorithms for inertial navigation.