INS/GPS data fusion technique utilizing radial basis functions neural networks

Most of the present navigation systems rely on Kalman filtering methods to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In general, INS/GPS integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and growth of position errors with time for INS. Present Kalman filtering INS/GPS integration techniques have several inadequacies related to sensor error model, immunity to noise and observability. This paper aims at introducing a multi-sensor system integration approach for fusing data from an INS and GPS hardware utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential global positioning system (DGPS). Although of being able the positioning accuracy, the complexity associated with both the architecture of multilayer perceptron networks and its online training algorithms limit the real time capabilities of this techniques. This article, therefore, suggests the use of an alternative ANN architecture. This architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedure than multi-layer perceptron networks. The INS and GPS data are first processed using wavelet multiresolution analysis (WRMA) before being applied to RBF network. The WMRA is used to compare the INS and GPS position outputs at different resolution levels. The RBF-ANN module is then trained to predict the INS position errors in real-time and provide accurate positioning of the moving platform. The field-test results have demonstrated that substantial improvement in INS/GPS positioning accuracy could be obtained by applying the combined WRMA and RBF-ANN modules.