Sensor Fusion for Magneto-Inductive Navigation

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, magnetoinductive 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 magnetoinductive signals with such complementary information. Further, we derive the Cramér-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 Cramér-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.

Analysis of the Principle, Facility and State-of-art Applications of Different Types Inductive Sensor

“…Wireless communication utilizes bioelectricity and frictional electricity as energy sources for electromagnetic sensors, and data transmission can be achieved through Bluetooth or WiFi, although this is still in the laboratory research stage. The integration of electromagnetic sensors with intelligent systems enables autonomous functionality, mainly achieved through sensor fusion and sensor integration, with MEMS integrated sensors being a typical example [20].…”

Factor Graph with Local Constraints: A Magnetic Field/Pedestrian Dead Reckoning Integrated Navigation Method Based on a Constrained Factor Graph

“…Geomagnetic vectors and an inertial navigation system were combined by Storms W et al [4] with a Kalman filter, and decimeter-level navigation was realized indoors to predict trajectories with the help of map information constraints. Chen Z et al [5] integrated geomagnetic vectors with pitch and roll angles to further calculate the gradient information of the magnetic field for trajectory matching, for which the error was at the level of 100 m. The Magneto-Inductive method was used by Wahlström J et al [6] to assist inertial navigation. Li et al [7] used magnetic fingerprint matching and heading angle correction for fusion navigation.…”

Effective Solution of the Problem of Electromagnetic Positioning Based on Two-Axial Radiator

“…In the cited works, the least squares method was not used, which allows us to hope to improve an accuracy for problems with the same input data if the least squares method is applied. The algorithm based on a three-axis radiator and the least squares method was proposed in [5] and developed in [6,7].Another approach [8] requires such an excessive number of electromagnetic field generators that the navigation problem is reduced to the usual measurement range problem [9].The above-mentioned works motivated to construct an algorithm of positioning a minimal configuration on the basis of two dipoles (a two-axis radiator) via the solution to the minimal system of size 6×6, which is done in the present paper. In fact, we propose a new electromagnetic positioning method on the basis of two magnetic dipoles with coincident centers.…”

“…In the cited works, the least squares method was not used, which allows us to hope to improve an accuracy for problems with the same input data if the least squares method is applied. The algorithm based on a three-axis radiator and the least squares method was proposed in [5] and developed in [6,7].…”