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Updated: Dec 7, 2025

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MS Location Estimation Based on the Artificial Bee Colony Algorithm.

Chien-Sheng Chen1, Jen-Fa Huang2, Nan-Chun Huang2

  • 1Department of Information Management, Tainan University of Technology, Tainan 701, Taiwan.

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|October 2, 2020
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Summary
This summary is machine-generated.

This study introduces an Artificial Bee Colony (ABC) algorithm to improve mobile station (MS) positioning accuracy. The method effectively minimizes non-line-of-sight (NLOS) errors, enhancing wireless location network efficiency.

Keywords:
artificial bee colony (ABC)base station (BS)mobile station (MS)non-line-of-sight (NLOS)time of arrival (TOA)

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Area of Science:

  • Wireless Communications
  • Optimization Algorithms
  • Geospatial Positioning

Background:

  • Accurate mobile station (MS) positioning is crucial in wireless communication networks.
  • Non-line-of-sight (NLOS) propagation introduces significant bias in location estimation.
  • Existing wireless location methods struggle with NLOS error suppression.

Purpose of the Study:

  • To develop a novel method for precise MS positioning using the Artificial Bee Colony (ABC) algorithm.
  • To quantify and mitigate positioning bias caused by NLOS scenarios.
  • To enhance the efficiency and accuracy of wireless location networks.

Main Methods:

  • Utilized three time-of-arrival (TOA) measurements to define an objective function for NLOS error quantification.
  • Applied the Artificial Bee Colony (ABC) algorithm to minimize the objective function and find the optimal MS location.
  • Conducted computer simulations to evaluate performance under various error distributions.

Main Results:

  • The proposed ABC algorithm-based positioning method demonstrated accurate MS location estimation.
  • Simulation results showed improved localization accuracy compared to existing methods.
  • The efficiency of the wireless location process was significantly increased.

Conclusions:

  • The Artificial Bee Colony algorithm effectively suppresses NLOS errors in MS positioning.
  • The developed method offers a more accurate and efficient solution for wireless location.
  • This approach enhances the reliability of mobile station localization in challenging environments.