GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference 2009
DOI: 10.1109/glocom.2009.5425278
|View full text |Cite
|
Sign up to set email alerts
|

Localization Using Radial Basis Function Networks and Signal Strength Fingerprints in WLAN

Abstract: Fingerprinting localization techniques provide reliable location estimates and enable the development of location aware applications especially for indoor environments, where satellite based positioning is infeasible. In our approach we utilize Received Signal Strength (RSS) fingerprints collected in known locations and employ a Radial Basis Function (RBF) neural network to approximate the function that maps fingerprints to location coordinates. We present a clustering scheme to reduce the size and computation… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
25
0
1

Year Published

2011
2011
2023
2023

Publication Types

Select...
4
3
3

Relationship

0
10

Authors

Journals

citations
Cited by 66 publications
(26 citation statements)
references
References 10 publications
0
25
0
1
Order By: Relevance
“…In positioning radial basis function was used for indoor localization in [13] where RSS fingerprints are fitted with corresponding positions, mathematical formulation of interpolation is given by:…”
Section: Multidimensional Data Interpolationmentioning
confidence: 99%
“…In positioning radial basis function was used for indoor localization in [13] where RSS fingerprints are fitted with corresponding positions, mathematical formulation of interpolation is given by:…”
Section: Multidimensional Data Interpolationmentioning
confidence: 99%
“…Such methods utilize a number of RSS fingerprints collected a priori at some predefined reference locations. Location can then be estimated by finding the best match between the currently measured fingerprint and reference fingerprints [1]- [4]. So far, the focus has been on improving accuracy, however the time required to estimate user location is also very important, because it affects the battery consumption of mobile devices.…”
Section: Introductionmentioning
confidence: 99%
“…and select any of the available positioning methods from the Algorithms configuration panel, which includes the deterministic K-Nearest Neighbor (KNN) and Weighted K-Nearest Neighbor (WKNN) algorithms [1], as well as the probabilistic Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE) algorithms [19]. The Airplace platform additionally supports two state-of-the-art methods developed in-house, namely the Radial Basis Function (RBF) networks [12] and the Subtract on Negative Add on Positive (SNAP) [13] approaches.…”
Section: Airplace Rss Logger Applicationmentioning
confidence: 99%