Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique

In this paper, a 3D indoor visible light positioning (VLP) system with fast computation time using received signal strength (RSS) is proposed and experimentally demonstrated. Assisted by the deep learning techniques, the complexity of the trilateration problem is greatly reduced, and the trilateration problem can be formulated as a linear mapping leading to faster position estimation than the conventional estimation. Moreover, a new method of off-line preparation is adopted to minimize the workload of the VLP system deployment for more practical usage. The proposition is implemented on an atto-cellular VLP unit, through which the real-time performance and positioning accuracy are demonstrated and validated in a 3D positioning experiment performed in a space of $1.2\times 1.2\times 2\,\,\mathrm {m}^{3}$ . The experimental results show that a positioning accuracy of 11.93 cm in confidence of 90% is achieved with 50 times faster the computation time compared to the conventional scheme.