Improved Extended Kalman Filter Estimation Using Threshold Signal Detection With an MEMS Electrostatic Microscanner

In this paper, a threshold signal detector is proposed to improve the state estimation accuracy of an extended Kalman filter (EKF) and is validated experimentally with a microelectromechanical system electrostatic microscanner. A first-order derivative of Gaussian 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 microscanner 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 under low signal-to-noise ratio conditions. A parametric study is conducted to examine sampling frequency and capacitance profile, among other factors that may affect detection error and EKF accuracy.