Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

TL;DR

This paper presents a novel online estimator for magnetometer and angular velocity sensor biases that improves heading accuracy in dynamic conditions without requiring instrument attitude, validated through simulations and field trials.

Contribution

It introduces a 15-state extended Kalman filter-based estimator for magnetometer and angular velocity biases that does not need attitude information and is effective in real-world scenarios.

Findings

Bias estimates converge rapidly to true values.

Bias compensation significantly improves heading accuracy.

Estimator works effectively in field trials with modest excitation.

Abstract

This article addresses the problem of dynamic on-line estimation and compensation of hard-iron and soft-iron biases of 3-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a 3-axis magnetometer and a 3-axis angular rate sensor. The proposed magnetometer and angular velocity bias estimator (MAVBE) utilizes a 15-state process model encoding the nonlinear process dynamics for the magnetometer signal subject to angular velocity excursions, while simultaneously estimating 9 magnetometer bias parameters and 3 angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with 3-axis accelerometer signals, are utilized to estimate bias compensated magnetic geodetic heading. Performance of the proposed MAVBE method is evaluated in comparison to the widely cited magnetometer-only TWOSTEP method in numerical simulations, laboratory experiments, and full-scale field trials of an instrumented autonomous underwater vehicle in the Chesapeake Bay, MD, USA. For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation accuracy.