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ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments.

Zhang Chen1,2, Jinlong Wang3

  • 1College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China. dcarp@126.com.

Sensors (Basel, Switzerland)
|June 2, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new indoor localization method, eigenspace-based DOA with direct-path recognition (ES-DPR), to accurately pinpoint signal sources despite indoor signal reflections. The method enhances positioning accuracy and robustness in challenging environments.

Keywords:
ES-DOAcoherent signal sourcedirect-path recognitiondirection of arrival (DOA)indoor passive positioninguniform linear antenna

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Area of Science:

  • Signal Processing
  • Wireless Communications
  • Indoor Localization

Background:

  • Indoor environments suffer from multipath propagation, degrading localization accuracy.
  • Existing Direction of Arrival (DOA) algorithms struggle with signal reflections and noise.

Purpose of the Study:

  • To propose a novel indoor passive localization approach, ES-DPR, to overcome multipath interference.
  • To enhance the accuracy and robustness of indoor positioning systems.

Main Methods:

  • Utilizing an eigenspace-based DOA (ES-DOA) algorithm with a uniform linear array (ULA) of omnidirectional antennas.
  • Employing a direct-path bearing recognition algorithm combining azimuth, RSS estimation, and ensemble learning.
  • Leveraging numerical simulations for validation.

Main Results:

  • The proposed ES-DPR method achieves high-resolution DOA estimation.
  • Demonstrates strong anti-noise capability in multipath environments.
  • The direct-path recognition algorithm proves reliable and robust across diverse indoor settings, even without direct path detection.

Conclusions:

  • ES-DPR offers a superior solution for indoor passive localization.
  • The method effectively mitigates multipath interference, improving positioning reliability.
  • The direct-path recognition component enhances robustness in complex indoor scenarios.