Bridging GPS outages of tightly coupled GPS/SINS based on the Adaptive Track Fusion using RBF neural network

Abstract

The tightly-coupled GPS/SINS integrated navigation system based on the adaptive multi-sensor track fusion algorithm utilizes the adaptive track fusion algorithm to fuse the output of the GPS/SINS Kalman filter and GPS Kalman filter. In case the GPS outages occur, accuracy of the system will be degraded rapidly due to the lack of measurements of the two Kalman filters. An algorithm of bridging GPS outages using radial basis function neural network is presented to improve accuracy of the navigation system during the GPS outages. This method uses radial basis function neural network to predict measurement of GPS/SINS Kalman filter during GPS outages in order to ensure the regular operation of the filter, resulting in reliable performance of the system. The performance of the method proposed is examined using intentionally introduced GPS outages and the mathematic simulation results have shown that the method proposed outperforms the navigation system without the neural network.