Visible light positioning becomes popular recently. However, its performance is degraded by the indoor diffuse optical channel. An artificial neural-network-based visible light positioning algorithm is proposed in this Letter, and a trained neural network is used to achieve positioning with a diffuse channel. Simulations are made to evaluate the proposed positioning algorithm. Results show that the average positioning error is reduced about 13 times, and the positioning time is reduced about two magnitudes. Moreover, the proposed algorithm is robust with a different field-of-view of the receiver and the reflectivity of the wall, which is suitable for various positioning applications.

Indoor visible light positioning becomes attractive due to the increasing demands of location-based services. This Letter proposes an indoor imaging visible light positioning scheme with a sampled sparse light source, image sensor, and gyro. An indoor positioning cellular with a single reference light source and an off-the-shelf mobile device is demonstrated. Experimental results show that the 3-dimensional positioning error is only several centimeters even with a rotated, rolled, and pitched mobile device. The proposed scheme is convenient and cost effective because the transmitter takes advantage of the existing lighting infrastructure and the receiver is a commercial mobile phone without any extra accessories.