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UWB-Assisted Bluetooth Localization Using Regression Models and Multi-Scan Processing.

Pan Li¹, Runyu Guan¹, Bing Chen²

¹School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China.

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|October 16, 2024

Summary

This summary is machine-generated.

This study introduces an ultra-wideband (UWB)-assisted Bluetooth method for indoor pedestrian positioning. It enhances accuracy by fusing multi-frame data to overcome multipath interference, achieving a low average error of 70 cm.

Keywords:

UWB cross-floor localization indoor tracking multi-frame fusion particle filtering

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Area of Science:

Indoor Positioning Systems
Wireless Sensor Networks
Signal Processing

Background:

Bluetooth devices are common for indoor navigation but suffer from multipath effects.
Existing methods struggle with accuracy in complex indoor environments due to signal reflections.
Accurate pedestrian localization is crucial for various applications, including robotics and augmented reality.

Purpose of the Study:

To propose an ultra-wideband (UWB)-assisted Bluetooth method for improved indoor pedestrian positioning.
To develop a robust approach for constructing a Bluetooth fingerprint library.
To enhance localization accuracy by mitigating multipath interference.

Main Methods:

Utilized ultra-wideband (UWB)-assisted Bluetooth signal strength acquisition for fingerprint library construction.
Developed a polynomial regression model to correlate signal strength with location.
Implemented a multi-frame fusion particle filtering approach for online pedestrian localization.

Main Results:

Achieved a highly accurate indoor pedestrian position estimation.
Reduced localization errors significantly compared to traditional methods.
Demonstrated an average positioning error as low as 70 cm.

Conclusions:

The proposed UWB-assisted Bluetooth method effectively improves indoor pedestrian localization accuracy.
Multi-frame fusion particle filtering successfully attenuates multipath interference.
This technique offers a promising solution for reliable indoor navigation systems.