Integration of INS and GPS using radial basis function neural networks for vehicular navigation

Navigation systems used in recent days rely mainly on Kalman filter to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In common, INS/GPS data fusion provides reliable navigation solution by overcoming drawbacks such as signal blockage for GPS and increase in position errors with time for INS. Kalman filtering INS/GPS integration techniques used in present days have some inadequacies related to the stochastic error models of inertial sensors, immunity to noise, and observability. This paper aims to introduce a new system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential GPS (DGPS). Though the integrated system using multi-layer perceptron scheme improves the positioning accuracy, it has shortcomings like complexity with respect to the architecture of multi-layer perceptron networks and limitation of online training algorithm to provide real-time capabilities. This paper, therefore, proposes the use of an alternative ANN architecture. The proposed architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks. The RBF-ANN module is trained to predict the INS position error and provide accurate positioning of the moving vehicle.