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A fast and precise indoor localization algorithm based on an online sequential extreme learning machine.

Han Zou1, Xiaoxuan Lu2, Hao Jiang3

  • 1School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore. zouhan@ntu.edu.sg.

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This study introduces an improved indoor positioning system (IPS) using an online sequential extreme learning machine (OS-ELM). This method enhances WiFi-based localization accuracy and adaptability to environmental changes.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Indoor positioning systems (IPS) are crucial for location-based services (LBS) in indoor environments.
  • WiFi-based IPS commonly use fingerprinting but face challenges with high offline survey costs and environmental inflexibility.
  • Existing WiFi IPS struggle with dynamic environmental changes, impacting localization accuracy.

Purpose of the Study:

  • To propose a novel indoor localization algorithm addressing the limitations of traditional WiFi-based IPS.
  • To leverage the fast learning and online sequential capabilities of extreme learning machines for improved IPS performance.
  • To reduce the time and manpower required for offline site surveys in WiFi IPS.

Main Methods:

  • Development of an indoor localization algorithm based on the online sequential extreme learning machine (OS-ELM).
  • Utilizing OS-ELM's rapid learning to minimize offline site survey efforts.
  • Employing OS-ELM's online sequential learning for real-time adaptation to environmental dynamics.
  • Conducting experiments simulating environmental changes like occupancy variations and door status modifications.

Main Results:

  • The proposed OS-ELM based algorithm demonstrates faster learning compared to traditional methods.
  • The algorithm effectively adapts to dynamic environmental changes, such as shifts in occupancy and door status.
  • Experimental results indicate higher localization accuracy compared to conventional WiFi-based IPS approaches.

Conclusions:

  • The OS-ELM based indoor localization algorithm offers a significant improvement over traditional methods.
  • Its ability to adapt quickly to environmental dynamics enhances localization accuracy in real-world scenarios.
  • This approach offers a more efficient and accurate solution for WiFi-based indoor positioning systems.