Aiming at the uncertainty of data collected in multi-sensor networks, a multi-sensor fusion technique based on the PSO algorithm is suggested to optimize the RBF neural network with the aim of reducing the uncertainty of data gathered in multi-sensor networks. The RBF neural network's weight and threshold parameters are modeled as moving particles, with vectors used to describe their positions. The PSO algorithm chooses the proper values for the parameters. The ideal parameter values of the RBF neural network are ultimately established following iterative training. It has been demonstrated that the PSO algorithm-based RBF neural network multi-sensor data fusion algorithm has higher fusion accuracy and shorter running times.

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