Three-dimensional visible light positioning using regression neural network

Three-dimensional visible light positioning (3D-VLP) is capable of achieving superior locating accuracy in comparison with other existing positioning techniques, such as global positioning system (GPS) and Wi-Fi-based method, which draws much attention from the researchers. In this paper, a novel 3D-VLP scheme using regression neural network is proposed to provide accurate and real-time positioning service. In the proposed method, the angle of arrival (AOA) vectors corresponding to the light-emitting diodes (LEDs) are obtained by the image sensor of the receiver and then fed into a regression neural network, which directly outputs the positioning results. Simulations are carried out to validate the superiority of the proposed method. It's observed that, in spite of the inevitable quantization error in the positioning process, the mean positioning error is still as accurate as 1.1 cm. In addition, the proposed positioning method is more robust to camera's height, and takes only 0.27ms to calculate the position, which could be used for real-time locating.