An Intuitive Approach to Inertial Sensor Bias Estimation

TL;DR

This paper introduces a simple, intuitive method for estimating inertial sensor biases that avoids complex algorithms like Kalman filters, using physical insights and a two-stage process validated on land vehicles.

Contribution

It presents a novel bias estimation technique based on physical intuition, decoupling the problem into two stages and requiring only three tunable parameters.

Findings

Effective bias estimation for low-cost MEMS sensors.

No need for Kalman filtering or complex algorithms.

Validated on moving land vehicles.

Abstract

A simple approach to gyro and accelerometer bias estimation is proposed. It does not involve Kalman filtering or similar formal techniques. Instead, it is based on physical intuition and exploits a duality between gimbaled and strapdown inertial systems. The estimation problem is decoupled into two separate stages. At the first stage, inertial system attitude errors are corrected by means of a feedback from an external aid. In the presence of uncompensated biases, the steady-state feedback rebalances those biases and can be used to estimate them. At the second stage, the desired bias estimates are expressed in a closed form in terms of the feedback signal. The estimator has only three tunable parameters and is easy to implement and use. The tests proved the feasibility of the proposed approach for the estimation of low-cost MEMS inertial sensor biases on a moving land vehicle.