A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things
Joint Authors
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-19
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
WLAN based localization is a key technique of location-based services (LBS) indoors.
However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear.
The traditional location estimation methods fail to provide fair estimation accuracy under the said environment.
We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA).
KLFDA extracts location features in a well-preserved kernelized space.
In the new kernel featured space, nonlinear RSS features are characterized effectively.
Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data.
By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted.
Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data.
Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.
American Psychological Association (APA)
Imran, Sajida& Ko, Young-Bae. 2018. A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1215939
Modern Language Association (MLA)
Imran, Sajida& Ko, Young-Bae. A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1215939
American Medical Association (AMA)
Imran, Sajida& Ko, Young-Bae. A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1215939
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215939