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Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity.

Shengxin Xu1, Heng Liu2, Fei Gao3

  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. xusx@bit.edu.cn.

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|January 26, 2019
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Summary
This summary is machine-generated.

This study enhances device-free localization using radio tomographic imaging (RTI) by employing spatial diversity and a novel image reconstruction method. The improved RTI significantly boosts localization accuracy and imaging performance in challenging indoor environments.

Keywords:
RSScompressive sensingindoor localizationradio tomographic imagingspatial diversity

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Radio tomographic imaging (RTI) is a device-free localization technology.
  • Indoor environments with multipath effects degrade RTI accuracy.
  • Existing RTI methods struggle with robust localization in cluttered spaces.

Purpose of the Study:

  • To improve the accuracy and robustness of device-free localization using RTI.
  • To enhance image quality and localization precision in challenging indoor settings.

Main Methods:

  • Utilizing spatial diversity with multiple antennas to improve radio frequency (RF) measurement quality.
  • Implementing a novel image reconstruction regularization combining sparsity and correlation.
  • Reducing image noise and enhancing imaging accuracy, especially with limited measurements.

Main Results:

  • Spatial diversity provides more reliable RF measurements by mitigating multipath effects.
  • The proposed reconstruction method significantly reduces image noise.
  • Achieved at least a 47% reduction in root-mean-square localization error compared to existing methods.

Conclusions:

  • The proposed RTI approach with spatial diversity and advanced reconstruction offers superior localization accuracy.
  • This method effectively addresses the limitations of RTI in cluttered indoor environments.
  • Demonstrated significant improvements in both imaging performance and localization error reduction.