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Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.

Jie Zhang1, Wendong Xiao2, Sen Zhang3

  • 1School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. zhangjie2009622@163.com.

Sensors (Basel, Switzerland)
|April 20, 2017
PubMed
Summary

Device-free localization (DFL) uses radio transmitters and receivers to track targets without electronic devices. A new Parameterized Geometrical Feature Extraction-Extreme Learning Machine (PGFE-ELM) approach significantly improves DFL accuracy and learning speed.

Keywords:
device-free localizationextreme learning machineparameterized geometrical feature extractionreceived signal strength

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Area of Science:

  • Wireless communication
  • Localization technologies
  • Machine learning applications

Background:

  • Device-free localization (DFL) enables target tracking without requiring devices on the target.
  • Radio-frequency (RF) DFL systems utilize radio transmitters and receivers to estimate target locations by analyzing received signal strength (RSS).

Purpose of the Study:

  • To propose an Extreme Learning Machine (ELM) approach for DFL to enhance localization efficiency and accuracy.
  • To introduce a novel feature extraction method for improved DFL performance.

Main Methods:

  • Developed a Parameterized Geometrical Feature Extraction (PGFE) method using geometrical intercepts and differential RSS measurements.
  • Integrated PGFE with ELM for DFL, training in an offline phase and performing real-time localization in an online phase.
  • The PGFE-ELM model allows for different wireless links during online and offline phases, enhancing robustness.

Main Results:

  • The proposed PGFE-ELM approach demonstrated significant improvements in localization accuracy and learning speed.
  • Outperformed existing DFL methods, including weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), and radio tomographic imaging (RTI).

Conclusions:

  • Parameterized Geometrical Feature Extraction combined with Extreme Learning Machine offers a robust and efficient solution for device-free localization.
  • The PGFE-ELM method shows superior performance compared to conventional machine learning and DFL techniques.