Cooperative Heading Estimation with von Mises-Fisher Distribution and Particle Filtering

Satellite navigation has become a widely used resource in many fields. However, satellite signals are vulnerable to different interference sources, both intentional and unintentional. In safety critical situations such as rescue and tactical operations a robust alternative for Global Navigation Satellite Systems (GNSS) is needed. In this work we use magnetometers and Ultra Wideband ranging together with inertial sensors in cooperative manner for heading estimation in infrastructure independent pedestrian navigation. We utilize von Mises-Fisher distribution in order to appropriately model directions, and use a particle filter for state estimation. We demonstrate our distributed and scalable approach in two real-life tests, partially GNSS-denied outdoor navigation scenario and indoor navigation scenario. The results show that with appropriate modeling it is possible to estimate heading in a robust manner and correct for heading drift typical in footmounted pedestrian dead reckoning.