Exploration of indoor environment perception and design model based on virtual reality technology - Zhang Mingli (Nonlinear Engineering)
This article combines the received signal strength indication ranging technology with the particle swarm backpropagation algorithm to propose an indoor environment perception and design model based on virtual reality. The highest detection rate of 95.5% was achieved in the static state, while a detection rate of 90% was maintained in the dynamic state, demonstrating strong robustness. At different distances, the positioning error of the PSO-BP-Adaboost algorithm remains within 0.82 m, indicating its effectiveness and accuracy in real-time indoor positioning. (a) Error changes before applying filtering algorithms. (b) Error changes after applying filtering algorithms.