A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT

Autor: Mahbub Hassan, Luca Luceri, Chun Tung Chou, Monica Nicoli, Marzieh Jalal Abadi
Jazyk: angličtina
Rok vydání: 2019
Předmět:
IoT
Magnetometer
Computer science
Perturbation (astronomy)
02 engineering and technology
Pedestrian
lcsh:Chemical technology
Machine learning
computer.software_genre
Biochemistry
Article
Analytical Chemistry
law.invention
law
Dead reckoning
0202 electrical engineering
electronic engineering
information engineering
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
business.industry
020206 networking & telecommunications
Sensor fusion
Atomic and Molecular Physics
and Optics
indoor localization
Pedestrian navigation
machine learning
context aware application
electrical_electronic_engineering
Context aware application
Indoor localization
Smart environments
smart environments
020201 artificial intelligence & image processing
Smart environment
Artificial intelligence
Internet of Things
business
computer
Zdroj: Sensors
Volume 19
Issue 21
Sensors (Basel, Switzerland)
Sensors, Vol 19, Iss 21, p 4609 (2019)
ISSN: 1424-8220
DOI: 10.3390/s19214609
Popis: This paper presents a system based on pedestrian dead reckoning (PDR) 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 novel 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 devices. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth&rsquo
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. To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. Compared to other methods in the literature, the method has the advantage of being infrastructure-free, fully distributed and robust to sensor failures thanks to the pre-filtering of perturbed measurements. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.
Databáze: OpenAIRE
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