scholarly journals Environment-Aware RSSI Based Positioning Algorithm for Random Angle Interference Cancellation in Visible Light Positioning System

Author(s):  
Yanqi Huang ◽  
Dayu Shi ◽  
Xun Zhang ◽  
El-Hassane Aglzim ◽  
Lina Shi
2019 ◽  
Vol 9 (6) ◽  
pp. 1238 ◽  
Author(s):  
Weipeng Guan ◽  
Xinjie Zhang ◽  
Yuxiang Wu ◽  
Zekun Xie ◽  
Jingyi Li ◽  
...  

Visible Light Positioning (VLP) is widely recognized as a cost-effective solution for indoor positioning with increasing demand. However, the nonlinearity and highly complex relationship between three-dimensional world coordinate and two-dimensional image coordinate hinders the good performance of image-sensor-based VLP. Therefore, there is a need to develop effective VLP algorithms to locate the positioning terminal using image sensor. Besides, due to the high computational cost of image processing, most existing VLP systems do not achieve satisfactory performance in terms of real-time ability and positioning accuracy, both of which are significant for the performance of indoor positioning system. In addition, the accurate identification of the ID information of each LED (LED-ID) is important for positioning, because if the LED-ID is not recognized well, the positioning can only be achieved in a particular positioning unit and cannot be applied to a large scene with many LEDs. Therefore, an effective image-sensor-based double-light positioning system is proposed in this paper to solve the above problems. We also set up relevant experiments to test the performance of the proposed system, which utilizes the rolling shutter mechanism of the Complementary Metal Oxide Semiconductor (CMOS) image sensor. Machine learning was used to identify the LED-ID for better results. Simulation results show that the proposed double-light positioning system could deliver satisfactory performance in terms of both the real-time ability and the accuracy of positioning. Moreover, the proposed double-light positioning algorithm has low complexity and takes the symmetry problem of angle into consideration, which has never been considered before. Experiments confirmed that the proposed double-light positioning system can provide an accuracy of 3.85 cm with an average computing time of 56.28 ms, making it a promising candidate for future indoor positioning applications.


2016 ◽  
Vol 55 (6) ◽  
pp. 066117 ◽  
Author(s):  
Heqing Huang ◽  
Lihui Feng ◽  
Peng Guo ◽  
Aiying Yang ◽  
Guoqiang Ni

Optik ◽  
2021 ◽  
pp. 166853
Author(s):  
Yong Chen ◽  
Zimiao Ren ◽  
Zhaozhong Han ◽  
Huanlin Liu ◽  
Qi-xiang Shen ◽  
...  

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha

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.


Sign in / Sign up

Export Citation Format

Share Document