Abstract: Aiming at the shortage of standard kalman filter for fault tolerant in integrated navigation, an intelligent method is proposed based on RBF neural network optimized by genetic algorithm for fault tolerant. In this method, which the uncertain noise effect is controlled by adjusting the filter gain in real-time so that the fault tolerant performance is improved. The design of hidden units and width of kernel function is important for the RBF neural network, so the number of hidden units and the width of kernel function are optimized by using the adaptive genetic algorithm. The centers of hidden units are calculated by using the K-mean clustering algorithm and the weights of output layer are calculated by using the least square algorithm. At last, the network structure is optimized, and the algorithm has high accuracy too. In order to test the effect of this method, the simulation based on the SINS/GPS integration navigation system is demonstrated, and it indicates this proposed method has stronger fault tolerant ability than the standard kalman filter under the condition of satisfying the navigation accuracy and adding less calculation. This also proves the availability of the proposed method.

Key words: InteGrated navigation, Radial basis function neural network, genetic algorithm, Intelligent fault tolerant