Bayesian Calibration of MEMS Accelerometers

Abstract

This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration through error-correcting functions. The parameters of these error-correcting functions are determined during a calibration process. However, due to various sources of noise, these parameters cannot be determined with precision, making it desirable to incorporate uncertainty in the calibration models. Bayesian modeling offers a natural and complete way of reflecting uncertainty by treating the model parameters as variables rather than fixed values. Additionally, Bayesian modeling enables the incorporation of prior knowledge, making it an ideal choice for calibration. Nevertheless, it is infrequently used in sensor calibration. This study introduces Bayesian methods for the calibration of MEMS accelerometer data in a straightforward manner using recent advances in probabilistic programming.

A Method of Precise Auto-Calibration in a Micro-Electro-Mechanical System Accelerometer

“…Recently, many research teams have dealt with this important issue. Various novel calibration techniques have been proposed, e.g., a lightweight method requiring low computational power [ 8 ], a Bayesian method [ 9 ], dynamic calibration [ 10 ], an improved ellipsoid fitting algorithm [ 11 ], a method using pseudo acceleration [ 12 ], and autonomous calibrations [ 13 , 14 ]. Moreover, new sensor designs have been developed, e.g., a sensor with digital automatic self-calibration [ 15 ] and a sensor with a recalibration feature [ 16 ].…”