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The dependence of proposed pedestrian navigation solutions on a dedicated infrastructure is a limiting factor to the deployment of location based services. Consequently self-contained Pedestrian Dead-Reckoning (PDR) approaches are gaining interest for autonomous navigation. Even if the quality of low cost inertial sensors and magnetometers has strongly improved, processing noisy sensor signals combined with high hand dynamics remains a challenge. Estimating accurate attitude angles for achieving long term positioning accuracy is targeted in this work. A new Magnetic, Acceleration fields and GYroscope Quaternion (MAGYQ)-based attitude angles estimation filter is proposed and demonstrated with handheld sensors. It benefits from a gyroscope signal modelling in the quaternion set and two new opportunistic updates: magnetic angular rate update (MARU) and acceleration gradient update (AGU). MAGYQ filter performances are assessed indoors, outdoors, with dynamic and static motion conditions. The heading error, using only the inertial solution, is found to be less than 10° after 1.5 km walking. The performance is also evaluated in the positioning domain with trajectories computed following a PDR strategy.

Magnetic, Acceleration Fields and Gyroscope Quaternion (MAGYQ)-Based Attitude Estimation with Smartphone Sensors for Indoor Pedestrian Navigation