  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.
