Sensor Fusion for Magneto-Inductive Navigation

TL;DR

This paper presents a sensor fusion approach combining magneto-inductive signals with inertial data to improve 3D navigation accuracy, achieving nearly twice the precision of previous methods through a maximum a posteriori estimator.

Contribution

It introduces a novel fusion method for magneto-inductive and inertial sensors, along with a Cramer-Rao bound analysis and distortion detection techniques.

Findings

Median position error reduced by nearly 50%

Estimator performance approaches the Cramer-Rao bound

Fusion with inertial sensors improves navigation accuracy

Abstract

Magneto-inductive navigation is an inexpensive and easily deployable solution to many of today's navigation problems. By utilizing very low frequency magnetic fields, magneto-inductive technology circumvents the problems with attenuation and multipath that often plague competing modalities. Using triaxial transmitter and receiver coils, it is possible to compute position and orientation estimates in three dimensions. However, in many situations, additional information is available that constrains the set of possible solutions. For example, the receiver may be known to be coplanar with the transmitter, or orientation information may be available from inertial sensors. We employ a maximum a posteriori estimator to fuse magneto-inductive signals with such complementary information. Further, we derive the Cramer-Rao bound for the position estimates and investigate the problem of detecting distortions caused by ferrous material. The performance of the estimator is compared to the Cramer-Rao bound and a state-of-the-art estimator using both simulations and real-world data. By fusing magneto-inductive signals with accelerometer measurements, the median position error is reduced almost by a factor of two.