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Observation Model for Indoor Positioning.

Berthold K P Horn1

  • 1Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA.

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Summary
This summary is machine-generated.

Improving WiFi-based indoor positioning, this study simplifies observation model fitting for Bayesian cell updates. A novel double exponential model and bootstrapping technique nearly double positioning accuracy, addressing signal obstructions in residences.

Keywords:
Bayesian gridFTMIEEE 802.11mcIEEE 802.11–2016RTTfine time measurementindoor locationindoor positionobservation modelrelative permittivityround trip timetransition model

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Area of Science:

  • Wireless communication
  • Indoor positioning systems
  • Signal processing

Background:

  • IEEE 802.11mc WiFi standard enables distance measurement between devices and access points (APs).
  • Traditional multilateration methods struggle with signal obstructions (walls, floors) in indoor environments.
  • Bayesian cell update shows promise for indoor positioning but requires an accurate observation model.

Purpose of the Study:

  • To simplify the fitting process for the observation model in Bayesian indoor positioning.
  • To develop and validate a more accurate observation model for WiFi-based distance measurements.
  • To enhance the accuracy of indoor positioning systems using WiFi round-trip time measurements.

Main Methods:

  • Reduced the two-dimensional observation model fitting problem to a one-dimensional problem.
  • Proposed and validated a 'double exponential' function as a suitable observation model.
  • Utilized 'bootstrapping' with Bayesian grid update results to generate accurate test data for model fitting.

Main Results:

  • The 'double exponential' observation model accurately fits real-world WiFi distance data.
  • Bootstrapping significantly simplifies test data generation, enabling accurate observation model creation.
  • A well-fitted observation model nearly doubles the accuracy of indoor positioning performance.

Conclusions:

  • The proposed methods simplify and improve the accuracy of WiFi-based indoor positioning.
  • The 'double exponential' model and bootstrapping are effective for indoor positioning challenges.
  • Addressing measurement bias in indoor WiFi ranging is crucial for further accuracy gains.