Electromagnetic radiation (EM) pollution has a certain impact on human life and health, and the reconstruction of the EM space field in this paper is of great practical significance for EM analysis and research. The radial basis function (RBF) sufficiently considers the influence of each sampling point and is more suitable for reconstructing the EM space field than other spatial interpolation methods. Currently, when RBF is used to reconstruct the EM space field, the optimal determination of the basis function and shape parameter (SP) is rarely considered. This ultimately leads to low reconstruction accuracy of the EM space field. Therefore, in this paper, the particle swarm optimization (PSO) is used to calculate the optimal SP of the RBF. On this basis, reliable EM space field reconstruction is performed, which helps people understand the EM distribution characteristics in actual situations from a visual perspective. The EM sampling data of a region on the Yunnan Normal University campus are used as the data source, and the RBF under the optimal parameters is used for EM reconstruction. The accuracy of its interpolation results is evaluated and compared and analyzed with inverse distance weighting (IDW) after distance index optimization. The results show that the RBF under optimal parameters reconstructs the EM space field with high accuracy and good effect, which can truly reflect the actual distribution of EM.
Keywords: Electromagnetic radiation; RBF; optimal shape parameter; space field reconstruction.
Electromagnetic radiation (EM) pollution has a great impact on the surrounding environment. Therefore, EM space field reconstruction can help us analyze the characteristics of the electromagnetic environment in a visual way. Radial Basis Function (RBF) is a method more suitable for EM space field reconstruction than other methods because it fully considers the influence of each sampling point. However, when currently using RBF to reconstruct the EM space field, few researchers consider how to choose the most appropriate basis function and shape parameter (SP). This results in low reconstruction accuracy. Therefore, this study uses particle swarm optimization (PSO) to find the optimal SP parameters for reliable EM space field reconstruction. The study used the EM sampling data of an area within the campus of Yunnan Normal University as the study material, and a parameter-optimized RBF method was adopted for the reconstruction of the EM space field. The reconstruction results were then evaluated for accuracy and compared and analyzed with the IDW method optimized with a distance index. Research results show that using RBF with optimal parameters to reconstruct the EM space field has high accuracy and can effectively reflect the actual EM distribution, thereby helping people better understand the characteristics of the electromagnetic environment.