Indoor High Precision Three-Dimensional Positioning System Based on Visible Light Communication Using Particle Swarm Optimization

Recently, visible light communication (VLC) has gradually become a research hotspot in indoor environments because its advantages of illumination and relative high positioning accuracy. But unfortunately, in the matter of algorithm complexity and positioning accuracy, most existing VLC-based systems fail to deliver satisfactory performance. Moreover, the majority of visible light positioning algorithm in them are based on two-dimensional (2-D) plane. In addition, some of the systems realize 3-D positioning on the base of various sensors or hybrid complex algorithm. These methods greatly reduce the robustness of VLC system. To solve these problems, a novel VLC positioning system based on modified particle swarm optimization (PSO) algorithm is put forward in this article. PSO is a powerful population-based stochastic approach to solve the global optimization problems, such as VLC-based indoor positioning, which can be transformed into a global optimization problem. Our simulation shows that the average distance error is 3.9 mm within 20 iterations in an indoor environment of ${\text{3}\,\text{m}}\times \text{3}\,{\text{m}}\times \text{4}\,{\text{m}}$. And the positioning results prove that this system can prove high localization accuracy and significantly lower the algorithm complexity. Moreover, in the experiment, we come up with a solution that using Kalman filter to deal with the unstable received signals. Our experiment result proves the mentioned system satisfies the requirement of cm-level indoor positioning. Therefore, this scheme may be considered as one of the competitive indoor positioning candidates in the future.