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Summary

This study introduces an indoor positioning system using affinity propagation clustering (APC) and Gaussian process regression (GPR). The method reduces the time and effort needed for radio signal learning, improving accuracy and efficiency.

Keywords:
affinity propagation clusteringbluetooth low energyfingerprinting localizationgaussian process regression

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Indoor positioning systems often rely on fingerprinting, which requires extensive data collection.
  • Traditional fingerprinting methods are time-consuming and labor-intensive due to signal learning.
  • Existing approaches face challenges in reducing offline workload and online computation costs.

Purpose of the Study:

  • To develop a fingerprinting localization system that minimizes offline workload and online computation.
  • To enhance indoor positioning accuracy and reliability using advanced machine learning techniques.
  • To address the limitations of traditional radio signal learning in fingerprinting localization.

Main Methods:

  • Utilized Gaussian Process Regression (GPR) for accurate received signal strength (RSS) prediction, including uncertainty estimation (variance).
  • Implemented Affinity Propagation Clustering (APC) to reduce the search space for reference points, optimizing localization.
  • Collected sparse RSS data from Bluetooth low energy beacons for system training and evaluation.

Main Results:

  • Demonstrated significant reduction in offline workload and computational cost compared to existing methods.
  • Achieved increased localization accuracy through GPR-based RSS prediction and APC-based clustering.
  • Experimental results from real-world deployments validated the system's effectiveness and efficiency.

Conclusions:

  • The proposed APC-based fingerprinting localization system with GPR offers a practical and efficient solution for indoor positioning.
  • This approach effectively balances reduced offline effort with low online computational demands.
  • The integration of GPR and APC enhances localization accuracy and system performance, outperforming conventional techniques.