A regression‐based methodology to improve estimation of inertial sensor errors using Allan variance data

Abstract

This research proposes a novel, autonomous, regression-based methodology for Allan variance analysis of inertial measurement unit (IMU) sensors. Current methods for Allan variance analysis have been rooted in the human-based interpretation of linear trends, referred to as the slope method. The slope method is so prolific; it is referenced among electrical and electronics engineering standards for IMU error analysis. However, the graphical nature and visual-inspection–based use of the method limit its ability to be programmed as a generalized algorithm, which hinders the autonomy desired in modern-day navigation computations. Using nonlinear regression with a ridge-regression initial guess, the proposed method is shown to produce comparable results to the gold standard slope method when using standard-length data collections and outperforms the slope method when the amount of available data is limited. This development directly enables accurate navigation solutions for all vehicles in land, air, sea, and space operations.