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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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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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Machine Learning Models for Indoor Positioning Using Bluetooth RSSI and Video Data: A Case Study.

Tomás Mamede1, Nuno Silva1, Eduardo R B Marques1,2

  • 1Department of Computer Science, Faculty of Sciences, University of Porto, Rua do Campo Alegre 1055, 4169-007 Porto, Portugal.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a multimodal Indoor Positioning System (IPS) combining Bluetooth signals and video. Ensemble machine learning significantly improved positioning accuracy in challenging museum environments.

Keywords:
ensemble learningindoor positioning systemmachine learningmultimodal data

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Area of Science:

  • Computer Science
  • Robotics
  • Electrical Engineering

Background:

  • Accurate indoor positioning is crucial for many applications.
  • Challenges include signal interference and environmental variability.
  • Existing systems often struggle with precision in complex settings.

Purpose of the Study:

  • To develop and evaluate a multimodal Indoor Positioning System (IPS).
  • To integrate Bluetooth Received Signal Strength Indicator (RSSI) and video data.
  • To enhance IPS accuracy using machine learning and ensemble techniques.

Main Methods:

  • Implemented a multimodal IPS using Bluetooth RSSI and video imagery.
  • Trained independent machine learning models for each data source.
  • Applied ensemble learning to combine model predictions.
  • Deployed and tested the system in a museum environment with deployment constraints.

Main Results:

  • Ensemble models significantly outperformed individual RSSI-based and video-based models.
  • Multimodal data integration improved positioning accuracy.
  • The system demonstrated effectiveness despite multipath interference, low lighting, and limited beacon infrastructure.

Conclusions:

  • Multimodal data fusion with ensemble learning enhances IPS accuracy in complex indoor environments.
  • The proposed system offers a robust solution for real-world deployments with practical constraints.
  • This approach shows significant potential for future indoor localization applications.