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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.
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.
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.
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