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Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking
Wenxu Wang1, Damián Marelli1,2, Minyue Fu1,3
1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel, Switzerland)
|May 24, 2020
Summary
This study introduces a new Wi-Fi indoor positioning method using channel state information (CSI) fingerprints. The technique improves accuracy by modeling signal behavior and combining it with motion data, outperforming existing approaches.
Area of Science:
- Engineering
- Computer Science
- Signal Processing
Background:
- Indoor positioning using Wi-Fi is cost-effective but suffers from multipath propagation, reducing accuracy.
- Channel State Information (CSI) fingerprints offer a way to enhance Wi-Fi based localization precision.
Purpose of the Study:
- To propose a novel indoor positioning method leveraging CSI fingerprints and environmental modeling.
- To improve localization accuracy by addressing signal distortions caused by multipath propagation.
Main Methods:
- A three-stage approach: 1. Building an environmental fingerprint model for interpolation. 2. Generating a preliminary position estimate using measured fingerprints. 3. Fusing the preliminary estimate with a receiver motion model for final positioning.
- Comparing the proposed method against rival techniques in scenarios with similar and altered environmental fingerprints.
Main Results:
- The proposed method demonstrated superior localization accuracy compared to existing techniques.
- Effective performance was observed even when environmental conditions changed from the initialization phase.
Conclusions:
- The novel three-stage positioning method effectively enhances indoor localization accuracy using Wi-Fi CSI fingerprints.
- The approach robustly handles environmental variations, offering a significant improvement over conventional methods.
