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Cited by 522

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

A comparative survey of WLAN location fingerprinting methods

Honkavirta, Ville, Perala, Tommi, Ali-Loytty, Simo, Piche, Robert·2009·2009 6th Workshop on Positioning, Navigation and Communication

Abstract

The term ldquolocation fingerprintingrdquo covers a wide variety of methods for determining receiver position using databases of radio signal strength measurements from different sources. In this work we present a survey of location fingerprinting methods, including deterministic and probabilistic methods for static estimation, as well as filtering methods based on Bayesian filter and Kalman filter. We present a unified mathematical formulation of radio map database and location estimation, point out the equivalence of some methods from the literature, and present some new variants. A set of tests in an indoor positioning scenario using WLAN signal strengths is performed to determine the influence of different calibration and location method parameters. In the tests, the probabilistic method with the kernel function approximation of signal strength histograms was the best static positioning method. Moreover, all filters improved the results significantly over the static methods.

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Metadata

Authors
Honkavirta, V., Perala, T., Ali-Loytty, S., & Piche, R.
Venue
2009 6th Workshop on Positioning, Navigation and Communication
Publisher
IEEE
Published
2009-03
Pages
243-251
OpenAlex
W1981373725
Cited by
522
References
15
FWCI
39.36
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A comparative survey of WLAN location fingerprinting methods. https://app.cochat.ai/papers/10.1109/wpnc.2009.4907834
The paper
Honkavirta, V., Perala, T., Ali-Loytty, S., & Piche, R. (2009). A comparative survey of WLAN location fingerprinting methods. 2009 6th Workshop on Positioning, Navigation and Communication. https://doi.org/10.1109/wpnc.2009.4907834

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.