Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/608390
Title: Indoor Localization and Radio Map Estimation Using Unsupervised Manifold Alignment with Geometry Perturbation
Authors: Khaqan Majeed;Sameh Sorour;Tareq Y. Al-Naffouri;Shahrokh Valaee
subject: radio map estimation|Indoor localization|manifold alignment
Year: 2016
Publisher: IEEE
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 percent 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 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m 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 percent performance improvement by using this information as compared to using only fingerprints.
Description: 
URI: http://localhost/handle/Hannan/140230
http://localhost/handle/Hannan/608390
ISSN: 1536-1233
volume: 15
issue: 11
Appears in Collections:2016

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Title: Indoor Localization and Radio Map Estimation Using Unsupervised Manifold Alignment with Geometry Perturbation
Authors: Khaqan Majeed;Sameh Sorour;Tareq Y. Al-Naffouri;Shahrokh Valaee
subject: radio map estimation|Indoor localization|manifold alignment
Year: 2016
Publisher: IEEE
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 percent 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 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m 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 percent performance improvement by using this information as compared to using only fingerprints.
Description: 
URI: http://localhost/handle/Hannan/140230
http://localhost/handle/Hannan/608390
ISSN: 1536-1233
volume: 15
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7362027.pdf1.25 MBAdobe PDFThumbnail
Preview File
Title: Indoor Localization and Radio Map Estimation Using Unsupervised Manifold Alignment with Geometry Perturbation
Authors: Khaqan Majeed;Sameh Sorour;Tareq Y. Al-Naffouri;Shahrokh Valaee
subject: radio map estimation|Indoor localization|manifold alignment
Year: 2016
Publisher: IEEE
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 percent 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 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m 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 percent performance improvement by using this information as compared to using only fingerprints.
Description: 
URI: http://localhost/handle/Hannan/140230
http://localhost/handle/Hannan/608390
ISSN: 1536-1233
volume: 15
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7362027.pdf1.25 MBAdobe PDFThumbnail
Preview File