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Errors in Global Positioning System

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An improved grey wolf optimization algorithm for 3-D UWB indoor positioning.

Jingmei Zhou¹, Bing Li^1,2,3, Shanshan Yang⁴

¹Hebei Normal University, Vocational and Technical School, Shijiazhuang, Hebei, China.

|June 18, 2026

Summary

This summary is machine-generated.

This study introduces an improved Grey Wolf Optimization (GWO) algorithm to enhance Ultra-wideband (UWB) indoor positioning accuracy, especially in non-line-of-sight (NLOS) environments. The novel approach significantly boosts positioning precision compared to existing methods.

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Area of Science:

Robotics and Automation
Wireless Communication Systems
Signal Processing

Background:

Traditional Ultra-wideband (UWB) positioning suffers from multipath effects and non-line-of-sight (NLOS) conditions, limiting accuracy.
Accurate indoor positioning is crucial for various applications, including robotics, asset tracking, and augmented reality.

Purpose of the Study:

To develop an improved Grey Wolf Optimization (GWO) algorithm for precise 3-D UWB indoor positioning in NLOS environments.
To enhance the accuracy and convergence speed of UWB positioning algorithms.

Main Methods:

Initial tag positioning using the Chan algorithm.
Constructing the GWO search area around the initial position.
Improving GWO through Tent chaotic mapping, a cosine-based nonlinear convergence factor, and dynamic inertia weights.

Main Results:

The improved GWO algorithm converges faster to the global optimal solution.
Achieved superior positioning accuracy in complex experimental settings.
Demonstrated significant average accuracy improvements of 62.92% over Chan, 66.43% over ChanTaylor, 45.71% over PSO, 40.91% over GWO, and 37.76% over AGWO.

Conclusions:

The proposed improved GWO algorithm offers higher practical value for 3-D UWB indoor positioning in NLOS environments.
This method effectively overcomes limitations of traditional algorithms, providing a more robust solution.