Initial alignment of inertial navigation system based on the raised-cosine RBF neural network

When applying the traditional Gauss RBF neural network to the modeling of initial alignment of inertial navigation systems, it is usually not ideal to approximate the value in the midway between two grid points. In order to solve this problem, a method for modeling the initial alignment of inertial navigation systems using raised-cosine RBF neural network was presented. A raised-cosine function is used as the radial basis function in the RBF neural network. It has good ability to approximate multivariable nonlinear system. Compared to traditional gauss RBF neural networks, the radial basis function of raised-cosine RBF neural network has more compact form. When calculation the output, it needs less radial basis functions at the time, thus reducing the calculate time effectively. The simulation results show that using raised-cosine RBF neural network for initial alignment in inertial navigation systems can not only obtain relatively high alignment accuracy, but also reduce the system calculating time.