Abstract
The normal Kalman filter (KF) is deficient in adaptive capability, at the same time, the estimation accuracy of the neural network (NN) filter is not very well and the performance depends on the artificial experience excessively. It is proposed to incorporate a back-propagation (BP) neural network into the adaptive federal KF configuration for the SINS/GPS/TAN (Terrain Auxiliary Navigation)/SAR (Synthetic Aperture Radar) integrated navigation system. The proposed scheme combines the estimation capability of adaptive KF and the learning capability of BP NN thus resulting in improved adaptive and estimation performance. This paper addresses operation principle, algorithm and key techniques. The simulation results show that the performance of the BP NN aided filter is better than the stand-alone adaptive Kalman filter’s.
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References
Xu, S.H., Wu, J.H., Hu, Z.Q.: Research on Wavelet Transform Method for the Faultdetection of the Integrated Navigation System. Journal of Astronautics 24, 111–114 (2003)
Chin, L.: Application of Neural Networks in Target Tracking Data Fusion. IEEE Trans. on Aerospace and Electronic Systems 30, 281–287 (1994)
Vaidehi, V., Chitra, N., Krishnan, C.N.: Neural Network Aided Kalman Filtering for Multitarget Tracking Application. In: IEEE Proc. of the Radar Conference, pp. 160–165 (1999)
Wong, Y.C., Sundareshan, M.K.: Data Fusion and Tracking of Complex Target Maneuvers with a Simplex-trained Neural Network-based Architecture. IEEE Proc. of the World Congress on Computational Intelligence 2, 1024–1029 (1998)
Xiong, S.S., Zhou, Z.Y.: Neural Filtering of Colored Noise Based on Kalman Filter Structure. IEEE Trans. on Instrumentation and Measurement 52, 742–747 (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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Chai, L., Yuan, J., Fang, Q., Kang, Z., Huang, L. (2004). Neural Network Aided Adaptive Kalman Filter for Multi-sensors Integrated Navigation. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_60
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DOI: https://doi.org/10.1007/978-3-540-28648-6_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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