10 October 2017 High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms
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Abstract
An indoor positioning algorithm based on visible light communication (VLC) is presented. This algorithm is used to calculate a three-dimensional (3-D) coordinate of an indoor optical wireless environment, which includes sufficient orders of multipath reflections from reflecting surfaces of the room. Leveraging the global optimization ability of the genetic algorithm (GA), an innovative framework for 3-D position estimation based on a modified genetic algorithm is proposed. Unlike other techniques using VLC for positioning, the proposed system can achieve indoor 3-D localization without making assumptions about the height or acquiring the orientation angle of the mobile terminal. Simulation results show that an average localization error of less than 1.02 cm can be achieved. In addition, in most VLC-positioning systems, the effect of reflection is always neglected and its performance is limited by reflection, which makes the results not so accurate for a real scenario and the positioning errors at the corners are relatively larger than other places. So, we take the first-order reflection into consideration and use artificial neural network to match the model of a nonlinear channel. The studies show that under the nonlinear matching of direct and reflected channels the average positioning errors of four corners decrease from 11.94 to 0.95 cm. The employed algorithm is emerged as an effective and practical method for indoor localization and outperform other existing indoor wireless localization approaches.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Weipeng Guan, Yuxiang Wu, Canyu Xie, Hao Chen, Ye Cai, Yingcong Chen, "High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms," Optical Engineering 56(10), 106103 (10 October 2017). https://doi.org/10.1117/1.OE.56.10.106103 . Submission: Received: 19 June 2017; Accepted: 6 September 2017
Received: 19 June 2017; Accepted: 6 September 2017; Published: 10 October 2017
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