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Author: Avi Bleiweiss

Affiliation: BShalem Research, United States

ISBN: 978-989-758-173-1

Keyword(s): Indoor Positioning System, WLAN Fingerprint, K-Means Clustering, Convolutional Extraction, KNN.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Sparsity ; Theory and Methods

Abstract: The proliferation of both wireless local area networks and mobile devices facilitated cost-effective indoor positioning systems that obviate the need for expensive infrastructure. We explore a floor-level, indoor localization system to predict the physical position of a mobile device holder in an office space by sensing a fingerprint of signal strength values, received from a plurality of wireless access points. In this work, we devise an instructive model that tailors elemental algorithms for unsupervised fingerprint learning, and resorts to only using a single-layer convolutional neural-network, succeeded by pooling. We applied our model to a fingerprint-based dataset that renders large multi-story buildings, and present a detailed analysis of the effect of changing setup parameters including the number of hidden nodes, the receptive field size, and the stride between extracted features. Our results surprisingly show that classification performance improves markedly with a sparser f eature extraction, and affirms a more intuitive gain, yet milder, as any of the number of features or the tile size increases. Despite its simplicity, the positional accuracy we attained is sufficient to provide a useful tool for a location-aware mobile application, purposed to automate the mapping of building occupants. (More)

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Paper citation in several formats:
Bleiweiss, A. (2016). A Mobile Indoor Positioning System Founded on Convolutional Extraction of Learned WLAN Fingerprints.In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 214-223. DOI: 10.5220/0005685702140223

@conference{icpram16,
author={Avi Bleiweiss.},
title={A Mobile Indoor Positioning System Founded on Convolutional Extraction of Learned WLAN Fingerprints},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={214-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005685702140223},
isbn={978-989-758-173-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Mobile Indoor Positioning System Founded on Convolutional Extraction of Learned WLAN Fingerprints
SN - 978-989-758-173-1
AU - Bleiweiss, A.
PY - 2016
SP - 214
EP - 223
DO - 10.5220/0005685702140223

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