ArticleIEEE transactions on industrial electronics (1982)2020
Improved Extended Kalman Filter Estimation using Threshold Signal Detection with a MEMS Electrostatic Microscanner.
Article in IEEE transactions on industrial electronics (1982), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Position Estimator Design for a MEMS Top-Drive Electrostatic Rotary Actuator.Sensors (Basel, Switzerland) · 2020Article
- Motion Estimation for a Compact Electrostatic Microscanner via Shared Driving and Sensing Electrodes in Endomicroscopy.IEEE/ASME transactions on mechatronics : a joint publication of the IEEE Industrial Electronics Society and the ASME Dynamic Systems and Control Division · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
A threshold signal detector is proposed to improve the state estimation accuracy of an extended Kalman filter (EKF) and is validated experimentally with a MEMS electrostatic micro-scanner. A first order derivative of Gaussian (DOG) filter is used to detect and locate rapid changes in voltage signal caused by crossing of a threshold angle determined by maximum overlap of capacitive electrodes. The event-triggered measurement is used in the update step of the EKF to provide intermittent but more accurate angle measurements than those of the capacitive sensor's continuous output. Experiments on the electrostatic micro-scanner show that with the threshold signal detector incorporated, the average position estimation accuracy of the EKF is improved by 15.1%, with largest improvement (30.3%) seen in low signal-to-noise ratio (SNR) conditions. A parametric study is conducted to examine sampling frequency and capacitance profile, among other factors that may affect detection error and EKF accuracy.
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