Research on visible light indoor positioning technique using two light sources and specular reflection cancellation

2019 ◽  
Vol 13 (7) ◽  
pp. 842-847 ◽  
Author(s):  
Wenqiang Zhang ◽  
Lifang Feng ◽  
Ping Che
Keyword(s):  
Visible Light ◽  
Specular Reflection ◽  
Indoor Positioning ◽  
Light Sources ◽  
Positioning Technique

scholarly journals In-Depth Analysis of Unmodulated Visible Light Positioning Using the Iterated Extended Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5198 ◽  
Author(s):  
Robin Amsters ◽  
Eric Demeester ◽  
Nobby Stevens ◽  
Peter Slaets
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Autonomous Robots ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Light Sources ◽  
High Speeds ◽  
Depth Analysis ◽  
Update Rate ◽  
Temporary Occlusion

Indoor positioning with visible light has become increasingly important in recent years. Usually, light sources are modulated at high speeds in order to wirelessly transmit data from the fixtures to a receiver. The accuracy of such systems can range from a few decimeters to a few centimeters. However, additional modulation hardware is required for every light source, thereby increasing cost and system complexity. This paper investigates the use of unmodulated light for indoor positioning. Contrary to previous work, a Kalman filter is used instead of a particle filter to decrease the computational load. As a result, the update rate of position estimation can be higher. Additionally, more resources could be made available for other tasks (e.g., path planning for autonomous robots). We evaluated the performance of our proposed approach through simulations and experiments. The accuracy depends on a number of parameters, but is generally lower than 0.5 m. Moreover, temporary occlusion of the receiver can be compensated in most cases.

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

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