MEMS IMU stochastic error modelling

Abstract

Low cost inertial measurement units (IMU) are comprised of micro-electro-mechanical systems (MEMS) gyroscopes and accelerometers. These inertial sensors have high random noise and time varying bias. Hence, an accurate stochastic error model is necessary to predict performance and also to implement an attitude and heading reference system or a navigator. The parameters of this stochastic model are classically obtained via Allan Variance analysis. In this paper, two modern approaches, based on autoregressive moving average model and Kalman Filter, respectively, are investigated and their performances are unveiled via sensitivity analysis, realistic simulations with typical MEMS parameters and also with experimental data. Basic equations for the estimation problem are developed and the solution sensitivity to data and parameters is discussed. It is shown that the deficiency with the online methods can be traced back to the difficulty in estimating the parameters B (bias instability) and K (rate random walk for gyros, acceleration random walk for accelerometers) separately under high measurement noise N (angular random walk for gyros, velocity random walk for accelerometers), which is typical of MEMS IMU.

Keywords

• System modelling
• state estimation
• time series analysis
• data modelling and estimation