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This paper focuses on applying a neural network model to predict pseudorange corrections (PRC) for differential Global Positioning System (DGPS). The class of nonlinear autoregressive recurrent neural networks is chosen as the basic architecture. The neural networks are trained by an unscented Kalman filter due to its powerful capabilities for online(More)
This paper presents a novel hybrid derivative-free extended Kalman filter, which takes advantage of both the linear time propagation of the Kalman filter and nonlinear measurement propagation of the derivative-free extended Kalman filter. The proposed filter is very suitable for the tightly coupled integration navigation system which consists of USBL or GPS(More)
A method using unscented Kalman filter for training radial-basis-function networks (RBFN) is studied. Unscented Kalman filter (UKF) shows great advantages than algorithms such as extended Kalman filter (EKF) and dual extended Kalman filter(DEKF) by extending the nonlinear functions using the second order approximation comparing to the one order in EKF and(More)
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