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Related Experiment Video

Updated: May 24, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
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An improved grey wolf algorithm and its localization research in complex indoor environments.

Bing Li1,2,3, Yanxin Hao1,2, Yiyang Cui1,2

  • 1College of Combustion Engineering, Hebei Normal University, Shijiazhuang, 050024, China.

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|March 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Grey Wolf Optimization (GWO) algorithm for ultra-wideband (UWB) indoor localization, significantly boosting accuracy in complex environments. The enhanced TIGWO algorithm overcomes limitations of traditional methods and swarm intelligence, achieving superior localization precision.

Keywords:
3D indoor localizationGrey wolf optimization algorithmLocalization accuracySwarm algorithmUltra-wideband technology

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Area of Science:

  • Robotics and Automation
  • Wireless Communication Systems
  • Optimization Algorithms

Background:

  • Complex indoor environments present significant challenges for traditional localization methods due to non-line-of-sight (NLOS) and multipath propagation issues.
  • Existing swarm intelligence optimization algorithms, while avoiding matrix inversions, suffer from slow convergence and local optima, limiting their practical application in localization.
  • Ultra-wideband (UWB) technology offers potential for high-accuracy indoor localization but requires robust algorithms to mitigate environmental interference.

Purpose of the Study:

  • To propose a novel and enhanced Grey Wolf Optimization (GWO) algorithm, termed TIGWO, for improving the accuracy of ultra-wideband (UWB) indoor localization.
  • To address the limitations of conventional localization techniques and existing swarm intelligence algorithms in complex indoor settings.
  • To validate the effectiveness of the TIGWO algorithm through experimental comparison with other established localization methods.

Main Methods:

  • Developed the TIGWO algorithm by integrating four key strategies: a small-area optimization using the Chan algorithm for initial localization, an improved Tent map, a nonlinear convergence factor, and a fitness-weighted location update with an out-of-bounds reflection mechanism.
  • Transformed the localization problem into a function optimization task solvable by the TIGWO algorithm, eliminating the need for matrix inversions.
  • Applied the TIGWO algorithm to determine the optimal tag location in an UWB indoor positioning system.

Main Results:

  • The proposed TIGWO algorithm demonstrated significant improvements in indoor localization accuracy compared to Chan, Chan-Taylor, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and standard GWO.
  • Achieved average localization accuracy enhancements of 59.65% over Chan, 63.41% over Chan-Taylor, 40.97% over PSO, 45.97% over WOA, and 35.44% over GWO.
  • In an equipment warehouse scenario, the TIGWO algorithm yielded low localization errors: 0.129 m on the X-axis, 0.101 m on the Y-axis, and 0.154 m on the Z-axis.

Conclusions:

  • The TIGWO algorithm effectively enhances indoor localization accuracy in complex environments by optimizing the GWO algorithm with targeted strategies.
  • The method successfully overcomes the limitations of traditional localization techniques and standard swarm intelligence algorithms, offering a more robust solution.
  • The experimental results confirm the superiority of the TIGWO algorithm for UWB indoor localization, showing substantial accuracy gains and low error margins.