Abstract
In this paper, we consider a practical indoor 3D mobile online visible light positioning (VLP) system, where the orientation of the UE is arbitrary. Based on the received signal strength (RSS) of multiple photo-detectors (PDs), we formulate the 3D VLP problem as a non-linear least squares (NLS) optimization problem, and then propose a sequential quadratic programming (SQP) positioning algorithm to efficiently calculate UE’s location. To obtain more accurate positioning solutions, we further leverage the advantages of deep learning and develop a stochastic gradient descent (SGD) based VLP algorithm, and achieve an average positioning error of 1.77cm, which significantly outperforms existing RSS VLP localization methods. Moreover, we design a 3D mobile online VLP system prototype by using a portable RaspberryPi 4 Model B as the positioning signal processor and data memory, and establish the first publicly available 3D VLP measured dataset including both RSS and orientation. The proposed positioning schemes are implemented and evaluated via the designed prototype system, which can achieve centimeter-level positioning accuracy (below 1 cm in certain condition).
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