Using lighting design tool to simplify the visible light positioning plan and reduce the deep learning loading

Abstract

We put forward and transform the commercially available lighting design software into an indoor visible light positioning (VLP) design tool. The proposed scheme can work well with different deep learning methods for reducing the loading of training data set collection. The indoor VLP models under evaluation include second order regression, fully-connected neural-network (FC-NN), and convolutional neural-network (CNN). Experimental results show that the similar positioning accuracy can be obtained when the indoor VLP models are trained with experimentally acquired data set or trained with software obtained data set. Hence, the proposed method can reduce the training loading for the indoor VLP.