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Nonlinear Regression Model of aLow-g MEMS Accelerometer

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3 Author(s)
Wei Tech Ang ; Sch. of Mech. & Aerosp. Eng., Nanyang Technol. Univ., Singapore ; Pradeep K. Khosla ; Cameron N. Riviere

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

Published in:

IEEE Sensors Journal  (Volume:7 ,  Issue: 1 )