Bayesian Calibration of MEMS Accelerometers
- This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical system (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. In addition, 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.
| Author: | Oliver DürrORCiDGND, Po-Yu Fan, Zong-Xian YinORCiD |
|---|---|
| DOI: | https://doi.org/10.1109/JSEN.2023.3272907 |
| ISSN: | 1530-437X |
| eISSN: | 1558-1748 |
| Parent Title (English): | IEEE Sensors Journal |
| Volume: | 23 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Document Type: | Article |
| Language: | English |
| Year of Publication: | 2023 |
| Release Date: | 2023/06/28 |
| Tag: | Accelerometer calibration; Bayesian parameter estimation; Gravity-based in-field calibration; Inertial measurement unit (IMU); Low-cost IMU calibration; Markov chain Monte Carlo (MCMC); Micro-electro-mechanical systems (MEMSs); Multiposition calibration; Uncertainty |
| Issue: | 12 |
| Page Number: | 8 |
| First Page: | 13319 |
| Last Page: | 13326 |
| Relevance: | Peer reviewed Publikation in Master Journal List |
| Open Access?: | Nein |
| Licence (German): |  Urheberrechtlich geschützt |
