Version 1
: Received: 13 August 2019 / Approved: 14 August 2019 / Online: 14 August 2019 (09:33:44 CEST)
How to cite:
Jalal Abadi, M.; Luceri, L.; Hassan, M.; Chou, C.T.; Nicoli, M. A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT. Preprints2019, 2019080164
Jalal Abadi, M.; Luceri, L.; Hassan, M.; Chou, C.T.; Nicoli, M. A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT. Preprints 2019, 2019080164
Jalal Abadi, M.; Luceri, L.; Hassan, M.; Chou, C.T.; Nicoli, M. A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT. Preprints2019, 2019080164
APA Style
Jalal Abadi, M., Luceri, L., Hassan, M., Chou, C.T., & Nicoli, M. (2019). A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT. Preprints. https://doi.org/
Chicago/Turabian Style
Jalal Abadi, M., Chun Tung Chou and Monica Nicoli. 2019 "A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT" Preprints. https://doi.org/
Abstract
This paper presents a system based on pedestrian dead reckoning for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple device. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth's magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
