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Localization Reliability Improvement Using Deep Gaussian Process Regression Model.

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This study introduces a new indoor positioning system using deep Gaussian process regression (DGPR). It significantly reduces data collection time and cost while maintaining accurate location estimation.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics Engineering

Background:

  • Indoor positioning systems are crucial due to the limitations of Global Positioning System (GPS) indoors.
  • Received Signal Strength (RSS) based methods are common but require extensive manual data collection for fingerprinting.
  • Existing methods are costly, time-consuming, and impractical for dynamic, large-scale indoor environments.

Purpose of the Study:

  • To develop a more efficient and cost-effective indoor positioning system.
  • To reduce the time and labor involved in creating and maintaining positioning databases.
  • To improve the accuracy and reduce computation time for indoor location estimation.

Main Methods:

  • Proposed an indoor positioning system utilizing a deep Gaussian process regression (DGPR) model.
  • DGPR is a nonparametric model requiring measurement of only a subset of reference points.
  • Converted RSS values into four characterizing features for input and employed reinforcement learning for optimization.

Main Results:

  • The DGPR model significantly reduced the time and cost associated with data collection.
  • Experimental results in simulated and physical environments demonstrated maintained positioning accuracy.
  • The proposed method also led to a reduction in the computation time for location estimation.

Conclusions:

  • The deep Gaussian process regression (DGPR) model offers an efficient solution for indoor positioning.
  • This approach mitigates the challenges of manual data collection in RSS-based systems.
  • The DGPR method provides a balance between accuracy and computational efficiency for indoor localization.