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

摘要

Aiming to improve positioning precision of the GPS/INS integrated navigation system during GPS outages, a novel model combined with strong tracking Kalman filter (STKF) and improved Radial Basis Function Neural Network(IRBFNN) algorithms is proposed and tested. STKF is used to estimate INS errors as a replacement of Kalman filter (KF), and IRBFNN is trained based on STKF when GPS works well and applied to predict INS errors during GPS outages. In the IRBF neural network, the width of the hidden layer and kernel function are optimized by using genetic algorithm to obtain a high precision generalization ability of RBF network structure. The simulation indicate that the proposed model can effectively provide high accurate corrections to the standalone INS during GPS outages.