{"title":"应用人工神经网络减小了电离层不规则性对GPS接收机的定位误差","authors":"S. Nontasud, N. Leelaruji","doi":"10.1109/ISCIT.2008.4700273","DOIUrl":null,"url":null,"abstract":"When the GPS signal pass through the ionosphere irregularities effect to rapid fluctuation of signal or scintillation occurred. This effect to the GPS receiver position error. This paper presents the error reduction of GPS receiver due to ionosphere irregularities by artificial neural network. The GPS position error depends on the strength of scintillation which measured in scintillation index or S4 index which due to total electron content (TEC) in the ionosphere. We used the measurement data which include the S4 index to analyze and correct the position error by artificial neural network. The result of this research we found the position error can reduce to less than 1 meter. The positions average close to correct position.","PeriodicalId":215340,"journal":{"name":"2008 International Symposium on Communications and Information Technologies","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Applied the artificial neural network for reduce the position error on GPS receiver due to ionospheric irregularities\",\"authors\":\"S. Nontasud, N. Leelaruji\",\"doi\":\"10.1109/ISCIT.2008.4700273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When the GPS signal pass through the ionosphere irregularities effect to rapid fluctuation of signal or scintillation occurred. This effect to the GPS receiver position error. This paper presents the error reduction of GPS receiver due to ionosphere irregularities by artificial neural network. The GPS position error depends on the strength of scintillation which measured in scintillation index or S4 index which due to total electron content (TEC) in the ionosphere. We used the measurement data which include the S4 index to analyze and correct the position error by artificial neural network. The result of this research we found the position error can reduce to less than 1 meter. The positions average close to correct position.\",\"PeriodicalId\":215340,\"journal\":{\"name\":\"2008 International Symposium on Communications and Information Technologies\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Symposium on Communications and Information Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCIT.2008.4700273\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Symposium on Communications and Information Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCIT.2008.4700273","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applied the artificial neural network for reduce the position error on GPS receiver due to ionospheric irregularities
When the GPS signal pass through the ionosphere irregularities effect to rapid fluctuation of signal or scintillation occurred. This effect to the GPS receiver position error. This paper presents the error reduction of GPS receiver due to ionosphere irregularities by artificial neural network. The GPS position error depends on the strength of scintillation which measured in scintillation index or S4 index which due to total electron content (TEC) in the ionosphere. We used the measurement data which include the S4 index to analyze and correct the position error by artificial neural network. The result of this research we found the position error can reduce to less than 1 meter. The positions average close to correct position.