Title :
Nonlinear Regression Model of aLow-g MEMS Accelerometer
Author :
Ang, Wei Tech ; Khosla, Pradeep K. ; Riviere, Cameron N.
Author_Institution :
Sch. of Mech. & Aerosp. Eng., Nanyang Technol. Univ., Singapore
Abstract :
This paper proposes a nonlinear regression model of a microelectromechanical systems capacitive accelerometer, targeted to be used in tilt sensing and low-g motion-tracking applications. The proposed model for the accelerometer´s deterministic errors includes common physical parameters used to rate an accelerometer: scale factor, bias, and misalignment. Simple experiments used to reveal the behavior and characteristics of these parameters are described. A phenomenological modeling method is used to establish mathematical representations of these parameters in relation to errors such as nonlinearity and cross-axis effect, without requiring a complete understanding of the underlying physics. Tilt and motion-sensing experiments show that the proposed model reduces sensing errors to a level close to the residual stochastic noise
Keywords :
accelerometers; capacitive sensors; microsensors; regression analysis; MEMS accelerometer; error modeling; inertial sensing; low-g motion-tracking; microelectromechanical systems capacitive accelerometer; nonlinear regression model; phenomenological modeling; stochastic noise; tilt sensing; Acceleration; Accelerometers; Mathematical model; Measurement units; Microelectromechanical systems; Micromechanical devices; Navigation; Physics; Robot kinematics; Testing; Accelerometer; error modeling; inertial sensing;
Journal_Title :
Sensors Journal, IEEE
DOI :
10.1109/JSEN.2006.886995