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Gaussian Process Regression Plus Method for Localization Reliability Improvement.

Kehan Liu1, Zhaopeng Meng2, Chung-Ming Own3

  • 1School of Computer Software, Tianjin University, Tianjin 300350, China. kehanliu@tju.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a new indoor positioning method using Gaussian processes and Naive Bayes. It achieves accurate location prediction while reducing computation time and costs associated with fingerprinting databases.

Keywords:
Gaussian Process RegressionNaive BayesRSS fingerprintinglocation estimation

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Area of Science:

  • Ubiquitous Computing
  • Context-Aware Applications
  • Indoor Positioning Systems

Background:

  • Location data is crucial for context-aware applications.
  • Existing indoor positioning systems often rely on Received Signal Strength (RSS) fingerprinting.
  • Manual RSS fingerprint database creation is costly and impractical for online prediction.

Purpose of the Study:

  • To develop an efficient and accurate indoor positioning method.
  • To overcome the limitations of manual RSS fingerprint database compilation.
  • To reduce computation time in location prediction environments.

Main Methods:

  • Utilized the Gaussian process method, a nonparametric model defined by mean function and covariance matrix.
  • Employed the Naive Bayes method for verification and simplification of precise predictions.
  • Conducted experiments in simulated and real environments at Tianjin University.

Main Results:

  • The proposed method successfully retained positioning accuracy.
  • Significant reduction in computation time for location predictions was observed.
  • Effectiveness was validated across varying data sizes and kernel types.

Conclusions:

  • The Gaussian process and Naive Bayes approach offers a viable alternative to traditional RSS fingerprinting.
  • This method enhances efficiency and reduces costs in indoor positioning.
  • The findings support the practical application of this technique in real-world scenarios.