An Inertial Navigation Framework for Indoor Positioning with Robust Heading
(English)Manuscript (preprint) (Other academic)
Indoor localization in unknown environments is considered, using inertial measurements from accelerometers, gyroscopes and magnetometers. Foot-mounted inertial sensors allow for stand-still detection triggering zero velocity updates that reduces the inertial navigation system (ins) drift in distance traveled from cubical to linear in time. We present a statistical framework, based on an navigation model. The standard stand-still mode is complemented with binary modes of magnetic disturbances. Test statistics for these two mode estimation problems are derived. Instead of making hard decisions, a hidden Markov model filter is used to compute the mode probabilities, leading to soft measurement updates in the Kalman filter.
Based on this, a robust smoothed heading estimate is computed in a second stage using the magnetometer. The final position estimate is then obtained by fusing the ins output with the robust heading in a standard dead-reckoning filter. Experiments demonstrate that the robust heading decreases the relative error in position from 10% to less than 1%, despite large magnetic disturbances.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-92131OAI: oai:DiVA.org:liu-92131DiVA: diva2:620222