Temperature compensation in high accuracy accelerometers using multi-sensor and machine learning methods

Temperature is a major source of inaccuracy in high-sensitivity accelerometers and gravimeters. Active thermal control systems require power and may not be ideal in some contexts such as airborne or spaceborne applications.
We propose a solution that relies on multiple thermometers placed within the accelerometer to measure temperature and thermal gradient variations. Machine Learning algorithms are used to relate the temperatures to their effect on the accelerometer readings. However, obtaining labeled data for training these algorithms can be difficult. Therefore, we also developed a training platform capable of replicating temperature variations in a laboratory setting.
Our experiments revealed that thermal gradients had a significant effect on accelerometer readings, emphasizing the importance of multiple thermometers.
The proposed method was experimentally tested and revealed a great potential to be extended to other sources of inaccuracy, such as rotations, as well as to other types of measuring systems, such as magnetometers or gyroscopes.

This work was funded by “Regione Lazio” (Italy) with European Regional Development Fund (Italy, Lazio) through the call “Gruppi di Ricerca 2020 (POR FESR LAZIO 2014 – 2020), project number: A0375-2020-36674

Experimental data are publicly available here: https://data.mendeley.com/datasets/f78bmhr628/1