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Indoor Positioning System Using Dynamic Model Estimation.

Yuri Assayag1, Horácio Oliveira1, Eduardo Souto1

  • 1Institute of Computing, Federal University of Amazonas, Manaus 69067-005, Amazonas, Brazil.

Sensors (Basel, Switzerland)
|December 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new model-based Indoor Positioning System (IPS) that dynamically estimates signal propagation parameters. This dynamic approach improves positioning accuracy by 17% compared to fixed models in large-scale environments.

Keywords:
bluetooth low energyindoor positioning systemslocalization systemspath-loss model

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Indoor Positioning Systems (IPSs) are crucial for locating mobile devices indoors.
  • Model-based IPSs offer advantages over fingerprinting by avoiding extensive environmental training.
  • Existing model-based IPSs often use fixed parameters, limiting accuracy in large-scale or non-uniform environments.

Purpose of the Study:

  • To propose a novel model-based IPS, Positioning using Dynamic Model Estimation (PoDME).
  • To enhance positioning accuracy by employing dynamic log-distance model parameters.
  • To introduce an optimized anchor node selection method for improved localization.

Main Methods:

  • PoDME estimates local log-distance model parameters using signal strengths from received signals.
  • A new method selects the three most effective anchor nodes for position computation.
  • The approach utilizes data from a large-scale, real-world Bluetooth experiment.

Main Results:

  • PoDME achieves a position estimation error of 3 meters.
  • This represents a 17% improvement over traditional fixed-parameter models.
  • The dynamic parameter estimation and anchor selection contribute to enhanced accuracy.

Conclusions:

  • PoDME offers a more accurate and adaptable solution for model-based indoor positioning.
  • Dynamic parameter estimation is key to overcoming limitations of fixed-parameter models in large scenarios.
  • The proposed anchor node selection method optimizes the performance of least-square-based position computation.