Research on Shipyard Integrated Navigation Algorithm based on Radial basis function neural network

In the case of signal masking, the accuracy of global positioning system (GPS) receiver output drops sharply, or even no output. At this time, inertial navigation (INS)/GPS navigation works in pure inertial mode, and the accuracy is relatively low. In order to enable INS/GPS integrated navigation to obtain high-precision navigation information even when the GPS receiver is not working, an improved radia basis function(RBF)neural network optimized by the particle swarm algorithm is proposed to assist Kalman filter(KF), which is combined with back-propagation(BP) neural network, the convergence of RBF neural network is simulated and compared, and the experimental results show that this method can effectively suppress the filter divergence when the GPS is out of lock, and improve the accuracy of navigation and positioning.