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http://dx.doi.org/10.12673/jant.2018.22.6.602

Predict DGPS Algorithm using Machine Learning  

Kim, HongPyo (Department of Aerospace Information Engineering, Konkuk University)
Jang, JinHyeok (Department of Aerospace Information Engineering, Konkuk University)
Koo, SangHoon (Department of Aerospace Information Engineering, Konkuk University)
Ahn, Jongsun (Navigation R&D Division, Korea Aerospace Research Institute)
Heo, Moon-Beom (Navigation R&D Division, Korea Aerospace Research Institute)
Sung, Sangkyung (Department of Aerospace Information Engineering, Konkuk University)
Lee, Young Jae (Department of Aerospace Information Engineering, Konkuk University)
Publication Information
Journal of Advanced Navigation Technology / v.22, no.6, 2018 , pp. 602-609 More about this Journal
Abstract
Differential GPS (DGPS) is known as a positioning method using pseudo range correction (PRC) which is communicating between a refence receiver and moving receivers. In real world, a moving receiver loses communication with the reference receiver, resulting in loss of PRC real-time communication. In this paper, we assume that the transmission of the pseudo range correction isinterrupted in the middle of real-time positioning situations, in which calibration information is received in the DGPS method. Under the disconnected communication, we propose 'predict DGPS' that real-time virtual PRC model which is modeled by a machine learning algorithm with previously acquired PRC data from a reference receiver. To verify predict DGPS method, we compared and analyzed positioning solutions acquired from real PRC and the virtual PRC. In addition, we show that positioning using the DGPS prediction method on a real road can provide an improved positioning solution assuming a scenario in which PRC communication was cut off.
Keywords
GPS; DGPS; Machine learning; Navigation; Nonlinear regression;
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