Jing Shan, W. Shao, Hong Xue, Yangli Xu, Danlei Mao
{"title":"基于Kriging空间插值的电磁环境图构建方法","authors":"Jing Shan, W. Shao, Hong Xue, Yangli Xu, Danlei Mao","doi":"10.1109/ICISCAE.2018.8666903","DOIUrl":null,"url":null,"abstract":"As an important tool for reflecting the status and situation of spectrum use in the electromagnetic environment, electromagnetic environment map has attracted wide attention in recent years. This paper proposes a method of electromagnetic environment map construction based on Kriging spatial interpolation. Firstly, the interference signal model is established with model-driven method and its correlation coefficient structure is analyzed to determine the model form of the variation function. Then, based on the data-driven approach, the measured data is used for the one-variable weighted regression method to estimate the parameters of the variation function model; Furthermore, based on the ordinary Kriging interpolation method, the optimal weighting value of each perceptual data point is calculated, and the data interpolation of each location of interest and the electromagnetic environment map in the area is generated. Compared with spatial geometric methods and functional methods, the method in this paper is based on the spatial autocorrelation analysis of global sampling points in the region, interpolation can be performed based on the spatial variation rules, and better interpolation accuracy can be obtained. Simulation results based on measured data and computer simulation data shows that the method in this paper has good accuracy and computational complexity, suitable for the construction of electromagnetic environment map.","PeriodicalId":129861,"journal":{"name":"2018 International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"The Method of Electromagnetic Environment Map Construction Based on Kriging Spatial Interpolation\",\"authors\":\"Jing Shan, W. Shao, Hong Xue, Yangli Xu, Danlei Mao\",\"doi\":\"10.1109/ICISCAE.2018.8666903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As an important tool for reflecting the status and situation of spectrum use in the electromagnetic environment, electromagnetic environment map has attracted wide attention in recent years. This paper proposes a method of electromagnetic environment map construction based on Kriging spatial interpolation. Firstly, the interference signal model is established with model-driven method and its correlation coefficient structure is analyzed to determine the model form of the variation function. Then, based on the data-driven approach, the measured data is used for the one-variable weighted regression method to estimate the parameters of the variation function model; Furthermore, based on the ordinary Kriging interpolation method, the optimal weighting value of each perceptual data point is calculated, and the data interpolation of each location of interest and the electromagnetic environment map in the area is generated. Compared with spatial geometric methods and functional methods, the method in this paper is based on the spatial autocorrelation analysis of global sampling points in the region, interpolation can be performed based on the spatial variation rules, and better interpolation accuracy can be obtained. Simulation results based on measured data and computer simulation data shows that the method in this paper has good accuracy and computational complexity, suitable for the construction of electromagnetic environment map.\",\"PeriodicalId\":129861,\"journal\":{\"name\":\"2018 International Conference on Information Systems and Computer Aided Education (ICISCAE)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Information Systems and Computer Aided Education (ICISCAE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCAE.2018.8666903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Information Systems and Computer Aided Education (ICISCAE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCAE.2018.8666903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Method of Electromagnetic Environment Map Construction Based on Kriging Spatial Interpolation
As an important tool for reflecting the status and situation of spectrum use in the electromagnetic environment, electromagnetic environment map has attracted wide attention in recent years. This paper proposes a method of electromagnetic environment map construction based on Kriging spatial interpolation. Firstly, the interference signal model is established with model-driven method and its correlation coefficient structure is analyzed to determine the model form of the variation function. Then, based on the data-driven approach, the measured data is used for the one-variable weighted regression method to estimate the parameters of the variation function model; Furthermore, based on the ordinary Kriging interpolation method, the optimal weighting value of each perceptual data point is calculated, and the data interpolation of each location of interest and the electromagnetic environment map in the area is generated. Compared with spatial geometric methods and functional methods, the method in this paper is based on the spatial autocorrelation analysis of global sampling points in the region, interpolation can be performed based on the spatial variation rules, and better interpolation accuracy can be obtained. Simulation results based on measured data and computer simulation data shows that the method in this paper has good accuracy and computational complexity, suitable for the construction of electromagnetic environment map.