A High-precision Indoor Visible Light Positioning System Under Ambient Light Sensor

The development of the Internet of Things and edge computing has promoted research on visible light positioning. Although the photodiode-based (PD-based) visible light positioning system can achieve low-cost positioning, problems exist, such as significant positioning errors, inconvenient receivers, and high power consumption. Therefore, in this paper, we propose a high-precision indoor visible light positioning system based on an ambient light sensor. The ambient light sensor has been widely embedded in various electronic devices, increasing the receiver's convenience. However, the ambient light sensor has a low sampling and inaccurate frequency when aliasing occurs. We use the undersampling method to acquire the transmitted beacon signal, obtain a frequency aliasing image through the Fast Fourier Transformation (FFT), and distinguish the different LEDs corresponding to different frequencies. Then, after identifying the LED beacon, we optimize the Lambertian order m and M to improve the position accuracy. Extensive experiments show that our proposed system can achieve sub-meter positioning accuracy.

I. Introduction

With the advent of the 5G era, the new media video industry's demand for large bandwidth, edge cloud, and AI continues to increase [1]–[3]. Indoor positioning and indoor navigation have a vast market. According to the Global Strategic Business Report, the global indoor positioning and indoor navigation (IPIN) market will reach $182.2 billion by 2030 [4]. Due to the limitations of the global positioning system (GPS) in indoor environments, indoor positioning technologies such as radio frequency, ZigBee, WiFi, and Bluetooth receive widespread attention. However, compared with these positioning technologies, visible light positioning has a low price, low power consumption, no susceptibility to electromagnetic interference, and high positioning accuracy [5]. Therefore, visible light has excellent prospects and applications in indoor positioning.