Tightly Integrated Motion Classification and State Estimation in Foot-Mounted Navigation Systems

Tightly Integrated Motion Classification and State Estimation in Foot-Mounted Navigation Systems

Skog, Isaac (Uppsala University)
Hendeby, Gustaf (Linköping University)
Kok, M. (TU Delft Team Manon Kok)

2023

A framework for tightly integrated motion mode classification and state estimation in motion-constrained inertial navigation systems is presented. The framework uses a jump Markov model to describe the navigation system's motion mode and navigation state dynamics with a single model. A bank of Kalman filters is then used for joint inference of the navigation state and the motion mode. A method for learning unknown parameters in the jump Markov model, such as the motion mode transition probabilities, is also presented. The application of the proposed framework is illustrated via two examples. The first example is a foot-mounted navigation system that adapts its behavior to different gait speeds. The second example is a foot-mounted navigation system that detects when the user walks on flat ground and locks the vertical position estimate accordingly. Both examples show that the proposed framework provides significantly better position accuracy than a standard zero-velocity aided inertial navigation system. More importantly, the examples show that the proposed framework provides a theoretically well-grounded approach for developing new motion-constrained inertial navigation systems that can learn different motion patterns.

Constant height detection
Filter bank
Inertial navigation
Motion-constraints
Zero-velocity detection

http://resolver.tudelft.nl/uuid:a6e34e6e-81c7-43fa-aecd-97a0f02c62ef

https://doi.org/10.1109/IPIN57070.2023.10332538

IEEE

2024-06-06

979-8-3503-2011-4

Proceedings of the 2023 13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023, 2023-09-25 → 2023-09-28, Nuremberg, Germany

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Funding Information: This work has been partially funded by the Swedish Research Council project 2020-04253 Tensor-field based localization and the Dutch Research Council (NWO) research program Veni project 18213 Sensor Fusion For Indoor Localisation Using The Magnetic Field.

Institutional Repository

conference paper

© 2023 Isaac Skog, Gustaf Hendeby, M. Kok