{"title":"GPS . NET RTK的对流层延迟模型","authors":"Qiu Lei, L. Lei, W. Zemin","doi":"10.1109/ITCS.2010.30","DOIUrl":null,"url":null,"abstract":"The network-based GPS technique provides a broad Spectrum of corrections for real-time kinematic. And the atmospheric refraction error is the main factors to be sufficient to support the ambiguity resolution (AR) and accuracy of the long-distance RTK. However, due to the strong spatial correlated of the tropospheric delay, the elevation difference between the reference plane and the rove station will cause the deviation of tropospheric error in the system so that the accuracy of troposphere correction will be lowered. In this paper, a new tropospheric error model based on neural network is presented. The neural network model takes into account not only the level factors, but also the elevation factor. It establishes the model in the spatial space. And the experimental results show that the accuracy of tropospheric delay model is within 5cm regardless of interpolation points in the network or network.","PeriodicalId":340471,"journal":{"name":"2010 Second International Conference on Information Technology and Computer Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"An Tropospheric Delay Model for GPS NET RTK\",\"authors\":\"Qiu Lei, L. Lei, W. Zemin\",\"doi\":\"10.1109/ITCS.2010.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The network-based GPS technique provides a broad Spectrum of corrections for real-time kinematic. And the atmospheric refraction error is the main factors to be sufficient to support the ambiguity resolution (AR) and accuracy of the long-distance RTK. However, due to the strong spatial correlated of the tropospheric delay, the elevation difference between the reference plane and the rove station will cause the deviation of tropospheric error in the system so that the accuracy of troposphere correction will be lowered. In this paper, a new tropospheric error model based on neural network is presented. The neural network model takes into account not only the level factors, but also the elevation factor. It establishes the model in the spatial space. And the experimental results show that the accuracy of tropospheric delay model is within 5cm regardless of interpolation points in the network or network.\",\"PeriodicalId\":340471,\"journal\":{\"name\":\"2010 Second International Conference on Information Technology and Computer Science\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second International Conference on Information Technology and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITCS.2010.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Information Technology and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCS.2010.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The network-based GPS technique provides a broad Spectrum of corrections for real-time kinematic. And the atmospheric refraction error is the main factors to be sufficient to support the ambiguity resolution (AR) and accuracy of the long-distance RTK. However, due to the strong spatial correlated of the tropospheric delay, the elevation difference between the reference plane and the rove station will cause the deviation of tropospheric error in the system so that the accuracy of troposphere correction will be lowered. In this paper, a new tropospheric error model based on neural network is presented. The neural network model takes into account not only the level factors, but also the elevation factor. It establishes the model in the spatial space. And the experimental results show that the accuracy of tropospheric delay model is within 5cm regardless of interpolation points in the network or network.