A novel neural-network-compensated Kalman filter for integrated navigation system was proposed. Based on the similarity of operation principle between Elman networks and non-linear ARMA model, the Elman network is employed as a compensating error estimator to improve accuracy of the Kalman filter. The proposed architecture is evaluated with the acquired data from a naval vessel. And the results show that the presented method can markedly attenuate the effect of interferes to Kalman filter, and improve the precision of the integrated navigation system. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Fu, J., Wang, Y., Li, J., Zheng, Z., & Yin, X. (2005). Recurrent networks for integrated navigation. In Lecture Notes in Computer Science (Vol. 3498, pp. 297–302). Springer Verlag. https://doi.org/10.1007/11427469_47
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