期刊
IEEE SENSORS JOURNAL
卷 12, 期 6, 页码 2100-2108出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2012.2182991
关键词
Accelerometer; Akaike information criterion; autocalibration; maximum likelihood; microelectromechanical systems (MEMS)
Up to now, little attention has been posed on a principled derivation of the cost function used for autocalibration of MEMS tri-axial accelerometers. By formulating the calibration problem in the context of maximum likelihood estimate, we derive here a general formulation that can be reduced to the classical quadratic cost function under certain hypotheses. Moreover, we adopt the Akaike information criterion to automatically choose the most adequate linear sensor model for the given calibration data set. Experiments on simulated and real data show the effectiveness of the proposed approach.
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