Indoor Localization and Radio Map Estimation using Unsupervised Manifold Alignment with Geometry Perturbation

Handle URI:
http://hdl.handle.net/10754/593342
Title:
Indoor Localization and Radio Map Estimation using Unsupervised Manifold Alignment with Geometry Perturbation
Authors:
Majeed, Khaqan; Sorour, Sameh; Al-Naffouri, Tareq Y. ( 0000-0003-2843-5084 ) ; Valaee, Shahrokh
Abstract:
The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15% of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1% of the fingerprinting load, some crowd sourced readings and plan coordinates of the indoor area. The 1% fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50% performance improvement by using this information as compared to using only fingerprints.
KAUST Department:
Electrical Engineering Program
Citation:
Indoor Localization and Radio Map Estimation using Unsupervised Manifold Alignment with Geometry Perturbation 2015:1 IEEE Transactions on Mobile Computing
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Mobile Computing
Issue Date:
22-Dec-2015
DOI:
10.1109/TMC.2015.2510631
Type:
Article
ISSN:
1536-1233
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362027
Appears in Collections:
Articles; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorMajeed, Khaqanen
dc.contributor.authorSorour, Samehen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorValaee, Shahrokhen
dc.date.accessioned2016-01-13T09:44:34Zen
dc.date.available2016-01-13T09:44:34Zen
dc.date.issued2015-12-22en
dc.identifier.citationIndoor Localization and Radio Map Estimation using Unsupervised Manifold Alignment with Geometry Perturbation 2015:1 IEEE Transactions on Mobile Computingen
dc.identifier.issn1536-1233en
dc.identifier.doi10.1109/TMC.2015.2510631en
dc.identifier.urihttp://hdl.handle.net/10754/593342en
dc.description.abstractThe Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15% of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1% of the fingerprinting load, some crowd sourced readings and plan coordinates of the indoor area. The 1% fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50% performance improvement by using this information as compared to using only fingerprints.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362027en
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectIndoor Localizationen
dc.subjectManifold Alignmenten
dc.subjectRadio Map Estimationen
dc.titleIndoor Localization and Radio Map Estimation using Unsupervised Manifold Alignment with Geometry Perturbationen
dc.typeArticleen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journalIEEE Transactions on Mobile Computingen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabiaen
dc.contributor.institutionElectrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON, M5S 3G4, Canadaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorAl-Naffouri, Tareq Y.en
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