INS/GPS Integration using Neural Networks for Land Vehicle Navigation Applications

Kai-Wei Chiang and Naser El-Sheimy

Abstract:	Most of the present navigation sensor integration techniques are based on Kalman filtering estimation procedures. Although Kalman filtering represents one of the best solutions for multi-sensor integration, it has some drawbacks in terms of stability, computation load, immunity to noise effects and observability. Furthermore, Kalman filters perform adequately only under certain predefined dynamic models. Neuron computing, a technology of Artificial Neural Network (ANN), is a powerful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. This article suggests a multi-sensor integration approach for fusing data from an Inertial Navigation system (INS) and Differential Global Positioning System (DGPS) utilizing multi-layer feed-forward neural networks with a back propagation learning algorithm. The performance of the proposed architecture was tested using two different INS systems(tactical grade IMU and navigation grade IMU) in a land vehicle.
Published in:	Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2002) September 24 - 27, 2002 Oregon Convention Center Portland, OR
Pages:	535 - 544
Cite this article:	Chiang, Kai-Wei, El-Sheimy, Naser, "INS/GPS Integration using Neural Networks for Land Vehicle Navigation Applications," Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2002), Portland, OR, September 2002, pp. 535-544.
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