Research on indoor space positioning using improved particle swarm optimization algorithm based on visible light communication

Abstract

In order to further improve the positioning accuracy of the indoor visible light positioning system, an improved particle swarm optimization (IPSO) algorithm is proposed. Considering the influence of noise, the positioning problem is transformed into the minimum distance evaluation problem. In the initial stage of the algorithm search, in order to enhance the global search ability of the algorithm, the sine function is used to increase the inertial weight value. In the later stage of the algorithm search, in order to enhance the local search ability of the algorithm, the cosine function is used to rapidly reduce the inertial weight value. At the same time, Gaussian limited density function is added to further improve the positioning accuracy of the algorithm. Finally, the positioning effectiveness of the proposed algorithm is verified through simulation and actual positioning experiments. The results show that, in the simulation phase, 95.2% of the positioning accuracy of the 5m×5m×3m positioning model is less than 3 cm, and the positioning error in the space below 2.2 m is less than 3 cm. The system overhead ranges from 0.12 s to 0.27 s, and the average system overhead is 0.19 s. In the experimental test stage, 92.78% of the positioning accuracy is less than 4 cm in the 1m×1m×1m positioning space, and 9.44% of the positioning accuracy reaches the millimeter level.