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 determines the positioning accuracy is transformed into the optimization problem of the minimum value of the objective function. Secondly, the dynamic assignment of inertia weight is 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 is reduced nonlinearly by sine function, and the value of group cognitive factors is increased linearly by cosine function, which further improves the positioning accuracy. Finally, the proposed localization algorithm is verified by simulation and experimental test. The results show that in the simulation test, in the two positioning models, the average spatial positioning errors of the four height planes of 0, 0.5, 1 and 1.5m are 0.65cm and 0.54cm respectively. In the experimental test, the average positioning errors in the built and indoor space are 2.67cm and 1.81cm respectively.