A robust stochastic magnetic field model for sensor network mapping

Magnetic localization systems based on passive permanent magnets (PM) are of great interest due to their ability to provide non-contact sensing and lack of a power requirement of the PM. One sub-problem of particular interest is accurately localizing, in real-time, a single magnetometer with unknown position and orientation, using a passive PM with controllable position and orientation. This is a challenging problem, mainly due to difficulty of designing a magnetic field model that allows high precision localization of a single sensor, but also has other qualities such as low computational complexity and robustness. In this work, we propose a stochastic magnetic field model, based on the dipole model, for the application of mapping a sensor network attached to an object with unknown position and shape. We validate the robustness of the model by testing it with different sensor network mapping configurations.