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An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite.

Lu Huang1,2, Xingli Gan3,4, Baoguo Yu5,6

  • 1State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China. 18642720668@163.com.

Sensors (Basel, Switzerland)
|October 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an indoor positioning system using pseudolite fingerprinting and a variational auto-encoding network. The system achieves high accuracy, with an average error of 0.39m, outperforming traditional methods.

Keywords:
array pseudolitecarrier phase differencedeep neural networkfingerprint matchingindoor localization

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Area of Science:

  • Engineering
  • Computer Science
  • Geomatics

Background:

  • Global Navigation Satellite System (GNSS) signals are unreliable indoors due to obstructions.
  • Pseudolites offer a viable alternative for indoor positioning by simulating satellite signals.
  • Existing pseudolite systems face challenges like integer ambiguity, initial position determination, and limited coverage.

Purpose of the Study:

  • To propose a novel indoor positioning system using homologous array pseudolite fingerprinting for enhanced accuracy.
  • To address limitations of traditional pseudolite positioning methods.
  • To develop a system capable of precise indoor localization.

Main Methods:

  • An offline phase involving carrier phase data collection and fingerprint database creation.
  • Utilizing a variational auto-encoding (VAE) network for feature clustering in the latent space.
  • Constructing a deep neural network to map carrier phase differences to indoor locations.
  • An online phase for real-time location prediction using the trained model.

Main Results:

  • The proposed system demonstrates superior performance in both dynamic and static indoor environments.
  • Achieved an average positioning accuracy of 0.39 meters.
  • The 95% positioning error was found to be less than 0.85 meters.
  • Outperformed traditional fingerprint positioning algorithms in experimental evaluations.

Conclusions:

  • The homologous array pseudolite fingerprinting system with VAE effectively overcomes indoor positioning challenges.
  • The developed deep learning approach significantly improves localization accuracy.
  • This method offers a robust and accurate solution for indoor positioning applications.