Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering
Joint Authors
Yang, Xiaolong
Wang, Yanmeng
Liu, Yiyao
Zhou, Mu
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-15
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Applications on Location Based Services (LBSs) have driven the increasing demand for indoor localization technology.
The conventional location fingerprinting based localization involves heavy time and labor cost for database construction, while the well-known Simultaneous Localization and Mapping (SLAM) technique requires assistant motion sensors as well as complicated data fusion algorithms.
To solve the above problems, a new pedestrian motion learning based indoor Wireless Local Area Network (WLAN) localization approach is proposed in this paper to achieve satisfactory LBS without the demand for location calibration or motion sensors.
First of all, the concept of pedestrian motion learning is adopted to construct users’ motion paths in the target environment.
Second, based on the timestamp relation of the collected Received Signal Strength (RSS) sequences, the RSS segments are constructed to obtain the signal clusters with the newly defined high-dimensional linear distance.
Third, the PageRank algorithm is performed to establish the hotspot mapping relations between the physical and signal spaces which are then used to localize the target.
Finally, the experimental results show that the proposed approach can effectively estimate the target’s locations and analyze users’ motion preference in indoor environment.
American Psychological Association (APA)
Yang, Xiaolong& Wang, Yanmeng& Zhou, Mu& Liu, Yiyao. 2018. Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1215912
Modern Language Association (MLA)
Yang, Xiaolong…[et al.]. Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1215912
American Medical Association (AMA)
Yang, Xiaolong& Wang, Yanmeng& Zhou, Mu& Liu, Yiyao. Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1215912
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215912