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Conference Paper: Spatio-temporal (S-T) similarity model for constructing WIFI-based RSSI fingerprinting map for indoor localization

TitleSpatio-temporal (S-T) similarity model for constructing WIFI-based RSSI fingerprinting map for indoor localization
Authors
KeywordsS-T similarity mode
WIFI fingerprinting
RSSI map
Characteristic metrics
Interpolation
Issue Date2014
Citation
The 5th International Conference on Indoor Positioning and Indoor Navigation, Busan, Korea, 27-30 October 2014. How to Cite?
AbstractWIFI-based received signal strength indicator (RSSI) fingerprinting is widely used for indoor localization due to desirable features such as universal availability, privacy protection, and low deployment cost. The key of RSSI fingerprinting is to construct a trustworthy RSSI map, which contains the measurements of received access point (AP) signal strengths at different calibration points. Location can be estimated by matching live RSSIs with the RSSI map. However, a fine-grained map requires much labor and time. This calls for developing efficient interpolation and approximation methods. Besides, due to environmental changes, the RSSI map requires periodical updates to guarantee localization accuracy. In this paper, we propose a spatio-temporal (S-T) similarity model which uses the S-T correlation to construct a fine-grained and up-to-date RSSI map. Five S-T correlation metrics are proposed, i.e., the spatial distance, signal similarity, similarity likelihood, RSSI vector distance, and the S-T reliability. This model is evaluated based on experiments in our indoor WIFI positioning system test bed. Results show improvements in both the interpolation accuracy (up to 7%) and localization accuracy (up to 32%), compared to four commonly used RSSI map construction methods, namely, linear interpolation, cubic interpolation, nearest neighbor interpolation, and compressive sensing.
Persistent Identifierhttp://hdl.handle.net/10722/217391

 

DC FieldValueLanguage
dc.contributor.authorZhu, Y-
dc.contributor.authorZheng, X-
dc.contributor.authorXu, J-
dc.contributor.authorLi, VOK-
dc.date.accessioned2015-09-18T05:58:17Z-
dc.date.available2015-09-18T05:58:17Z-
dc.date.issued2014-
dc.identifier.citationThe 5th International Conference on Indoor Positioning and Indoor Navigation, Busan, Korea, 27-30 October 2014.-
dc.identifier.urihttp://hdl.handle.net/10722/217391-
dc.description.abstractWIFI-based received signal strength indicator (RSSI) fingerprinting is widely used for indoor localization due to desirable features such as universal availability, privacy protection, and low deployment cost. The key of RSSI fingerprinting is to construct a trustworthy RSSI map, which contains the measurements of received access point (AP) signal strengths at different calibration points. Location can be estimated by matching live RSSIs with the RSSI map. However, a fine-grained map requires much labor and time. This calls for developing efficient interpolation and approximation methods. Besides, due to environmental changes, the RSSI map requires periodical updates to guarantee localization accuracy. In this paper, we propose a spatio-temporal (S-T) similarity model which uses the S-T correlation to construct a fine-grained and up-to-date RSSI map. Five S-T correlation metrics are proposed, i.e., the spatial distance, signal similarity, similarity likelihood, RSSI vector distance, and the S-T reliability. This model is evaluated based on experiments in our indoor WIFI positioning system test bed. Results show improvements in both the interpolation accuracy (up to 7%) and localization accuracy (up to 32%), compared to four commonly used RSSI map construction methods, namely, linear interpolation, cubic interpolation, nearest neighbor interpolation, and compressive sensing.-
dc.languageeng-
dc.relation.ispartofInternational Conference on Indoor Positioning and Indoor Navigation-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectS-T similarity mode-
dc.subjectWIFI fingerprinting-
dc.subjectRSSI map-
dc.subjectCharacteristic metrics-
dc.subjectInterpolation-
dc.titleSpatio-temporal (S-T) similarity model for constructing WIFI-based RSSI fingerprinting map for indoor localization-
dc.typeConference_Paper-
dc.identifier.emailZhu, Y: yxzhu@eee.hku.hk-
dc.identifier.emailXu, J: jlxu@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturepostprint-
dc.identifier.hkuros254341-

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