Novel hybrid of strong tracking Kalman filter and improved radial basis function neural network for GPS/INS integrated navagation

2016 ◽  
Author(s):  
Xiao Chun Tian ◽  
Cheng Dong Xu
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis

scholarly journals Research on a mixed prediction method to vehicle integrated navigation systems

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988525
Author(s):  
Di Zhao ◽  
Huaming Qian ◽  
Dingjie Xu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Global Positioning System ◽  
Basis Function ◽  
Unscented Kalman Filter ◽  
Positioning System ◽  
Global Positioning ◽  
Radial Basis

Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.

Research on Learning Algorithm of RBF Neural Network Based on Extended Kalman Filter

2014 ◽  
Vol 989-994 ◽  
pp. 2705-2708
Author(s):  
Xu Sheng Gan ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Extended Kalman Filter ◽  
Basis Function ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Learning Capability ◽  
Radial Basis ◽  
Neural Network Algorithm

To improve the learning capability of Radial Basis Function (RBF) neural network, a RBF neural network algorithm based on Extended Kalman Filter (EKF) is proposed. First the basic idea of EKF algorithm and RBF neural network are introduced, and then EKF is used to optimize the parameters combination of RBF neural network to obtain the better model. The experiment proves its feasibility.

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