High‐Precision Indoor Visible Light Positioning Method for Line‐Of‐Sight Scenes Based on a Spatiotemporal Sequence Attention Mechanism

ABSTRACT Using deep learning to improve the accuracy of indoor visible light positioning (VLP) systems has gradually become a widely used research strategy in the field. However, current deep learning‐based indoor visible light localization algorithms have not been able to effectively mine the deep temporal and spatial sequence features in signals, resulting in complex network construction and low localization accuracy. To address this issue, the text proposes a deep learning framework that utilizes an attention mechanism to train a small number of randomly continuously sampled spatial received signals to predict the coordinates of the received signals and encode the spatiotemporal sequence attributes of the received signals as a feature into the data, constructed a highly reliable spatiotemporal sequence attention mechanism for indoor visible light localization method. Combined with Convolutional Neural Networks (CNN), the localization accuracy is further improved. Through simulation experiments, it has been verified that the neural network structure designed in this paper has better positioning accuracy compared to advanced algorithms, and can still achieve centimeter‐level (9.886cm) average positioning error under low signal‐to‐noise ratio (SNR) conditions. It is proved that the method proposed in this paper is reliable in the indoor VLP system.