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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Related Experiment Video

Updated: Aug 1, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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Crowdsourced Indoor Positioning with Scalable WiFi Augmentation.

Yinhuan Dong1, Guoxiong He1, Tughrul Arslan1

  • 1School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, UK.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances indoor positioning by augmenting WiFi fingerprint databases using virtual reference points and spatial modeling. The proposed method improves accuracy and significantly reduces computational complexity, especially in areas with sparse data.

Keywords:
WiFi fingerprintingaugmentationcrowdsourcingindoor positioningmachine learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics Engineering

Background:

  • Crowdsourcing WiFi signals for indoor positioning databases is efficient but sensitive to crowd density, leading to accuracy degradation in sparse areas.
  • Existing methods struggle with data scarcity, impacting the reliability of indoor positioning systems.

Purpose of the Study:

  • To propose a scalable WiFi fingerprint augmentation method to improve indoor positioning accuracy.
  • To address the limitations of crowdsourced data by generating virtual reference points and modeling spatial WiFi signals.

Main Methods:

  • Developed a two-module augmentation method: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM).
  • Proposed globally self-adaptive (GS) and locally self-adaptive (LS) approaches within VRPG to identify potential unsurveyed reference points (RPs).
  • Employed a multivariate Gaussian process regression (MGPR) model to estimate WiFi signal distributions and predict signals for new RPs.

Main Results:

  • Combining GS and MGPR improved positioning accuracy by 5%–20% with halved computation complexity compared to conventional methods.
  • Combining LS and MGPR reduced computation complexity by 90% while still offering moderate accuracy improvements.
  • Evaluations on an open-source dataset demonstrated the effectiveness of the proposed augmentation techniques.

Conclusions:

  • The proposed scalable WiFi fingerprint augmentation method effectively enhances indoor positioning accuracy.
  • The VRPG and SWSM modules, particularly with MGPR, offer significant improvements in both accuracy and computational efficiency.
  • The adaptive approaches (GS and LS) provide flexibility in balancing accuracy gains and computational load for WiFi-based indoor positioning.