학술논문

RatioVLP: Ambient Light Noise Evaluation and Suppression in the Visible Light Positioning System
Document Type
Periodical
Source
IEEE Transactions on Mobile Computing IEEE Trans. on Mobile Comput. Mobile Computing, IEEE Transactions on. 23(5):5755-5769 May, 2024
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Costs
LED lamps
Calibration
Mobile computing
Robustness
Power demand
Time-varying systems
Indoor navigation
visible light positioning
ambient light interference
photodiodes
noise suppression
Language
ISSN
1536-1233
1558-0660
2161-9875
Abstract
Visible Light Positioning (VLP), a promising indoor positioning technique, has gained wide popularity worldwide because of its ubiquitous infrastructure, low power consumption, and high positioning precision. However, VLP systems based on photodiodes (PDs) often suffer from varying ambient light with time and space, which seriously degrades their positioning precision and robustness. In this article, we carefully evaluate the influence of the ambient light on the VLP system, which includes the reduction of positioning accuracy by varying ambient light with time and the inaccurate parameter calibration by unevenly distributed ambient light. Then, we figure out that the influence of ambient light on the Received Signals Strength (RSS) values is determined by the ambient light intensity and PD, which is independent of external factors, including distance, frequency, LED, etc. Next, we propose a new positioning framework, RatioVLP, where a ratio model that is more robust to varying ambient light with time is used. However, the ratio model is severely dependent on the Lambert parameters that are vulnerable to ambient light, which reduces the framework's precision when the calibration area is unevenly covered by ambient light. Thus, we design new parameters that are less sensitive to ambient light, called R parameter , to connect the RSS ratio and its corresponding distance ratio, which can strengthen the ratio model's robustness and effectively reduce the influence of ambient light on the parameter calibration process. Experimental results show that the positioning precision of the proposed method is improved by more than 50 % when compared to the conventional Lambert model in scenes influenced by ambient light.