In order to improve the positioning accuracy of the current indoor visible light positioning system, an improved particle swarm optimization algorithm considering the dynamic inertia weight and cognitive factors of noise interference is proposed. Firstly, the Euclidean distance that determined the positioning accuracy was transformed into the optimization problem of the minimum value of the objective function. Secondly, the dynamic assignment of inertia weight was used to enhance the global search ability in the initial stage and the local search ability in the later stage of PSO. Then, the value of individual cognitive factors was reduced nonlinearly by sine function, and the value of group cognitive factors was increased linearly by cosine function, which further improved the positioning accuracy. Finally, the proposed localization algorithm was verified by simulation and experimental test. The results show that in the simulation test, in the 5m×5m×5m and 5m×4m×3m positioning models, the average spatial positioning errors of the four height planes of 0, 0.5, 1 and 1.5m are 0.65 and 0.54cm respectively. In the experimental test, the average positioning errors in the 1m×1m×0.8m and 1m×0.8m×0.8m indoor space are 2.67 and 1.81cm respectively.