Journal Article10.1109/TVT.2015.2397598
Distance-Based Interpolation and Extrapolation Methods for RSS-Based Localization With Indoor Wireless Signals
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TL;DR: It is shown that both the horizontal positioning accuracy and the floor detection probability can be improved with proper interpolation and extrapolation methods, and it is also illustrated that the best positioning performance is not necessarily achieved with the best average interpolationand extrapolation accuracy.
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Abstract: Wireless local area network (WLAN)-based fingerprinting using received signal strength (RSS) has been considered to be one solution for indoor positioning. However, one widely recognized problem in fingerprinting is the collection and maintenance of a proper fingerprint database. In this paper, we consider having an incomplete fingerprint database with realistic coverage gaps, and we study the performance of several interpolation and extrapolation methods for recovering the missing fingerprint data. For this purpose, we have collected an extensive set of data at frequency bands of 2.4 and 5 GHz from one university building with four floors. The accuracy of the interpolation and extrapolation methods is studied by artificially removing fingerprints from the database using a randomized procedure and by comparing the estimated fingerprints with the original fingerprints. The average RSS estimation error of different interpolation and extrapolation methods is shown for various percentages of missing fingerprints. In addition, a cumulative RSS error distribution is studied to reveal the dispersion of the error statistics, which affect the user positioning accuracy. Here, the user positioning accuracy is defined in terms of horizontal positioning error and floor detection probability. The user positioning accuracy is also compared in four cases, namely when using the original fingerprints, the partial fingerprints, the interpolated fingerprints, and the interpolated and extrapolated fingerprints. It is shown that both the horizontal positioning accuracy and the floor detection probability can be improved with proper interpolation and extrapolation methods. However, it is also illustrated that the best positioning performance is not necessarily achieved with the best average interpolation and extrapolation accuracy, but it is important to avoid certain types of errors in interpolation and extrapolation.
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Citations
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges
TL;DR: This paper compares some of the representative localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity and depicts illustrative evaluation of the approaches in the literature and guide to future improvement opportunities.
325
A Meta-Review of Indoor Positioning Systems.
TL;DR: This paper provides an introduction to IPS and the different technologies, techniques, and some methods commonly employed and serves as a guide for the reader to easily find further details on each technology used in IPS.
266
Advances on localization techniques for wireless sensor networks
TL;DR: The main goal in this paper is to present the state-of-the-art research results and approaches proposed for localization in WSNs by considering a wide variety of factors and categorizing them in terms of data processing, routing, algorithms, etc.
180
Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning
Elena Simona Lohan,Joaquín Torres-Sospedra,Helena Leppäkoski,Philipp Richter,Zhe Peng,Joaquín Huerta +5 more
- 03 Oct 2017
TL;DR: A new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, is presented and some benchmark indoor positioning results using these data are presented.
175
An improved inertial/wifi/magnetic fusion structure for indoor navigation
TL;DR: This paper proposes a dead-reckoning (DR)/WiFi fingerprinting/magnetic matching (MM) integration structure that uses off-the-shelf sensors in consumer portable devices and existing WiFi infrastructures and reduces the rate of mismatches by over 75.0% when compared with previous DR/WiFi/MM integration structures.
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