Online Allan Variance Noise Coefficient Estimation Method Based on Iterative Least Squares

The Allan variance analysis method is a widely used technique for evaluating random noise errors in Micro-ElectroMechanical System Inertial Measurement Units (MEMS-IMUs). However, the conventional Allan variance analysis method has limitations, such as requiring pre-collection and storage of large amounts of data and being unable to compute random error coefficients in real time. To address these issues, this paper models the common random noise errors in MEMS-IMUs and, building upon the batch-processing computation method used in conventional Allan variance analysis, proposes an online Allan variance noise coefficient estimation method based on iterative least squares. This method enables real-time processing of sensor data, dynamic computation of Allan variance, and estimation of random noise coefficients. The proposed method was tested using both simulated data and real-world data collected from a Murata SCHA634-D03 MEMS-IMU. Experimental results demonstrate that the method can effectively estimate random noise error coefficients with rapid convergence, verifying its feasibility and superiority.