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- W2037108571 startingPage "277" @default.
- W2037108571 abstract "The combination of Inertial Navigation System (INS) and Global Positioning System (GPS) provides superior performance in comparison with either a stand-alone INS or GPS. However, the positioning accuracy of INS/GPS deteriorates with time in the absence of GPS signals. A least squares support vector machines (LS-SVM) regression algorithm is applied to INS/GPS integrated navigation system to bridge the GPS outages to achieve seamless navigation. In this method, LS-SVM is trained to model the errors of INS when GPS is available. Once the LS-SVM is properly trained in the training phase, its prediction can be used to correct the INS errors during GPS outages. Simulations in INS/GPS integrated navigation showed improvements in positioning accuracy when GPS outages occur." @default.
- W2037108571 created "2016-06-24" @default.
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- W2037108571 date "2013-07-01" @default.
- W2037108571 modified "2023-09-23" @default.
- W2037108571 title "Seamless INS/GPS Integration Based on Support Vector Machines" @default.
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- W2037108571 doi "https://doi.org/10.4028/www.scientific.net/amm.336-338.277" @default.
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