Developing a noise/error model for inertial sensors, in particular accelerometers.
Overly simplified models such as white noise are not adequate for high-accuracy applications. For example, random walk components of the noise are identified. Characteristics such as Allan variance are commonly determined for accelerometers and gyros. More on these in Farrell, Jay A., Felipe O. Silva, Farzana Rahman, and Jan Wendel. “IMU Error State Modeling for State Estimation and Sensor Calibration: A Tutorial,” May 20, 2020. https://escholarship.org/uc/item/1vf7j52p.
Some intro to random processes and related stuff can be found in Farrell, Jay. Aided Navigation: GPS with High Rate Sensors. McGraw-Hill, Inc., 2008.
There is even support for this in Matlab: https://www.mathworks.com/help/nav/ref/imusensor-system-object.html. Learning from their docs may give some guidance even if ultimately we do not use their toolbox.
As an outcome of this task, several relevant components of the accelerometer noise will be identified and experimentally characterized. This model will later be used as one of the inputs to estimators such as some advanced versions of Kalman filter.