The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Kernel-Based Positioning in Wireless Local Area Networks
Abstract
The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods.
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Cited by
Au, A. W. S., Feng, C., Valaee, S., Reyes, S., Sorour, S., Markowitz, S. N., Gold, D., Gordon, K., & Eizenman, M. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2012.175 · Cited by 183
Machine Learning Algorithm for Wireless Indoor Localization
Abdullah, O. A., & Abdel-Qader, I. (2018) · Machine Learning - Advanced Techniques and Emerging Applications · doi:10.5772/intechopen.74754 · Cited by 14
Metadata
- Authors
- Kushki, A., Plataniotis, K. N., & Venetsanopoulos, A. N.
- Venue
- IEEE Transactions on Mobile Computing
- Published
- 2007-06
- Volume
- 6
- Issue
- 6
- Pages
- 689-705
- ISSN
- 1536-1233 (ISSN-L)
- OpenAlex
- W2144723957
- Cited by
- 425
- References
- 42
- FWCI
- 17.01
- Type
- journal article
- Language
- en
- Open access
- closed
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