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Improved Extended Kalman Filter Estimation using Threshold Signal Detection with a MEMS Electrostatic Microscanner.

Yi Chen1, Haijun Li2, Zhen Qiu3

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

IEEE Transactions on Industrial Electronics (1982)
|August 9, 2021
PubMed
Summary
This summary is machine-generated.

A novel threshold signal detector improves state estimation accuracy for extended Kalman filters (EKF). This method enhances MEMS micro-scanner angle measurements, especially in noisy conditions.

Keywords:
Kalman filtermicrosensorssignal processing

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Area of Science:

  • Control Systems Engineering
  • Microelectromechanical Systems (MEMS)

Background:

  • State estimation accuracy is critical for MEMS device performance.
  • Extended Kalman Filters (EKF) are widely used but can suffer from noise and continuous measurement limitations.

Purpose of the Study:

  • To propose and validate a threshold signal detector for enhancing EKF state estimation accuracy.
  • To improve the performance of MEMS electrostatic micro-scanners.

Main Methods:

  • Implemented a first-order derivative of Gaussian (DOG) filter for rapid voltage signal change detection.
  • Utilized event-triggered measurements in the EKF update step.
  • Experimentally validated the system with a MEMS electrostatic micro-scanner.

Main Results:

  • Achieved a 15.1% average improvement in EKF position estimation accuracy.
  • Observed a significant 30.3% improvement in low signal-to-noise ratio (SNR) conditions.
  • Conducted a parametric study on sampling frequency and capacitance profile.

Conclusions:

  • The threshold signal detector effectively enhances EKF accuracy for MEMS micro-scanners.
  • Event-triggered measurements provide more accurate intermittent data than continuous sensor output.
  • The proposed method offers substantial benefits, particularly in challenging, noisy environments.