Nonlinear Regression Model of a Low-g MEMS Accelerometer

Wei-Tech Ang, Pradeep Khosla, and Cameron Riviere
IEEE Sensors Journal, Vol. 7, No. 1, January, 2007, pp. 81-88.


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Abstract
This paper proposes a nonlinear regression model of a microelectromechanical systems capacitive accelerometer, targeted to be used in tilt sensing and low- 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
accelerometer, error modeling, inertial sensing

Notes
Sponsor: NIH, NSF
Grant ID: R01EB000526, EEC-9731748
Associated Center(s) / Consortia: Medical Robotics Technology Center
Associated Lab(s) / Group(s): Surgical Mechatronics Laboratory
Associated Project(s): Micron: Intelligent Microsurgical Instruments
Number of pages: 8

Text Reference
Wei-Tech Ang, Pradeep Khosla, and Cameron Riviere, "Nonlinear Regression Model of a Low-g MEMS Accelerometer," IEEE Sensors Journal, Vol. 7, No. 1, January, 2007, pp. 81-88.

BibTeX Reference
@article{Ang_2007_5984,
   author = "Wei-Tech Ang and Pradeep Khosla and Cameron Riviere",
   title = "Nonlinear Regression Model of a Low-g MEMS Accelerometer",
   journal = "IEEE Sensors Journal",
   pages = "81-88",
   month = "January",
   year = "2007",
   volume = "7",
   number = "1",
}