Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

We propose a physical arrangement consisting of a 2.4 GHz WiFi radio, and high attenuation surfaces with an air-gap in-between, to delimit a fingerprinting region. We show by simulations, and field measurements, that such arrangement allows to form a characteristic lobe in the radiation pattern after the gap, which is characterized by an abrupt change in the Received Signal Strengths (RSSs) along the direction parallel to the surfaces. From measured RSS samples, we construct, to aid our analysis, an equation that approximates the radiation pattern as a continuous RSS radio map, using symbolic regression. Finally, we observe the positioning performance of this arrangement, performing RSS fingerprint pattern matching with a Neural Network. Results are compared to the positioning accuracy that would be obtained by using an omnidirectional radiation pattern antenna. We conclude that, the abrupt change in RSSs obtained with this arrangement, translates into a better positioning accuracy, close and along a direction parallel to the surfaces.

Original languageEnglish
Title of host publication2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)978-1-7281-1788-1
ISBN (Print)978-1-7281-1789-8
DOIs
Publication statusPublished - Sep 2019
MoE publication typeA4 Article in a conference publication
Event2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019 - Pisa, Italy
Duration: 30 Sep 20193 Oct 2019

Conference

Conference2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019
CountryItaly
CityPisa
Period30/09/193/10/19

Keywords

  • Accuracy
  • Air-gap
  • Indoor positioning
  • Neural Network
  • Proximity Sensor
  • Radiation Pattern
  • RSS fingerprinting
  • Symbolic Regression
  • WiFi
  • OtaNano

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    Lembo, S., Horsmanheimo, S., Somersalo, M., Laukkanen, M., Tuomimaki, L., & Huilla, S. (2019). Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap. In 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019 [8911820] IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/IPIN.2019.8911820