Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Fu, J., Wang, Y., Li, J., Zheng, Z., Yin, X. (2005). Recurrent Networks for Integrated Navigation. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_47
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DOI: https://doi.org/10.1007/11427469_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)