Fusion Based on Visible Light Positioning and Inertial Navigation Using Extended Kalman Filters
Abstract
:1. Introduction
2. System Configuration and Algorithm
2.1. System Design
2.2. VLC Positioning Algorithm
2.3. Inertial Navigation Algorithm
2.4. Fusion Position Algorithm Based on the Kalman Filter
3. Field Experiment and Results
3.1. Experiment Setup
3.1.1. Structure of the Fusion Positioning System
3.1.2. Experiment Environment
3.2. Experiment Results
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Error | VLC | EKF |
---|---|---|
Maximum | 0.619 m | 0.411 m |
Average | 0.339 m | 0.145 m |
Minimum | 0.167 m | 0.137 m |
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Li, Z.; Feng, L.; Yang, A. Fusion Based on Visible Light Positioning and Inertial Navigation Using Extended Kalman Filters. Sensors 2017, 17, 1093. https://doi.org/10.3390/s17051093
Li Z, Feng L, Yang A. Fusion Based on Visible Light Positioning and Inertial Navigation Using Extended Kalman Filters. Sensors. 2017; 17(5):1093. https://doi.org/10.3390/s17051093
Chicago/Turabian StyleLi, Zhitian, Lihui Feng, and Aiying Yang. 2017. "Fusion Based on Visible Light Positioning and Inertial Navigation Using Extended Kalman Filters" Sensors 17, no. 5: 1093. https://doi.org/10.3390/s17051093
APA StyleLi, Z., Feng, L., & Yang, A. (2017). Fusion Based on Visible Light Positioning and Inertial Navigation Using Extended Kalman Filters. Sensors, 17(5), 1093. https://doi.org/10.3390/s17051093