Abstract
Based on improved back propagation (BP) neural network, information fusion state estimation problem for multi-sensor system is considered. Firstly, particle swarm optimization, search dynamic learning rate and additional momentum method are introduced to train the initial weights and thresholds of BP neural network. Then, the improved neural network is used to optimize the estimated value of Kalman filter. Finally, the sate estimators are fused by weighting matrices. A simulation example verifies the effectiveness of the proposed algorithm.
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References
L. Chai, J.P. Yuan, Q. Fang, L.W. Huang, Neural-network-aided information fusion for integrated navigation application. J. Northwest. Polytech. Univ. 23(1), 45–48 (2005)
J. Cheng, J.H. Park, J.D. Cao, W.H. Qi, Hidden Markov model-based nonfragile state estimation of switched neural network with probabilistic quantized outputs. IEEE Trans. Cybern. (2019). https://doi.org/10.1109/TCYB.2019.2909748
P. Gao, Research on the improved neural network algorithm in nonlinear system identification. M.Sc. Dissertation (Heilongjiang University, China, 2018)
Z.L. Jing, H. Xu, G.Z. Dai, X.Q. Zhou, Neural network-based information fusion and parallel adaptive tracking of maneuvering targets. Acta Aeronaut. Astron. Sin. 16(6), 715–719 (1995)
M. Katzfussa, J.R. Stroudb, C.K. Wiklec, Understanding the ensemble Kalman filter. Am. Stat. 70(4), 350–357 (2016)
M.T. Liu, Z.Z. Tian, H.Q. Qi, C.C. Zhang, X.M. Liu, Cooperative fusion model based on Kalman-BP neural network for suspended sediment concentration measurement. J. Basic Sci. Eng. 24(5), 970–977 (2016)
F.J. Pei, H.H. Ju, P.Y. Cui, Initial alignment method based on Kalman/UKF integrated training neural network. J. Syst. Simul. 20(23), 6494–6497 (2008)
L.J. Song, Z.X. Duan, B. He, Z. Li, Application of federal Kalman filter with neural networks in the velocity and attitude matching of transfer alignment. Complexity 2, 1–7 (2018)
S.L. Sun, Z.L. Deng, Multi-sensor optimal information fusion Kalman filter. Automatica 40, 1017–1023 (2004)
F.C. Sun, R. Xiong, H.W. He, A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl. Energy 162, 1399–1409 (2016)
Q. Sun, X.B. Hui, H. Huang, An adaptive Kalman tracking filter with colored noises. Fire Control Command Control 36(3), 49–51 (2011)
D.Y. Wang, H.Y. Luo, O. Grunder, Y.B. Lin, H.X. Guo, Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Appl. Energy 190, 390–407 (2017)
G.C. Wang, X.S. Xu, Y.Q. Yao, J.W. Tong, A novel BPNN-based method to overcome the GPS outages for INS/GPS system. IEEE Access 7, 82134–82143 (2019)
F. Zhang, H.F. Xue, Y.H. Zhang, A new BP neural network fusion algorithm for multi-source remote sensing data on groundwater. Appl. Ecol. Environ. Res. 17(4), 9083–9095 (2019)
W.X. Zhang, BP neural network forecasting model based on PSO. M.Sc. dissertation (Harbin Institute of Technology, China, 2010)
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This work was supported by the National Natural Science Foundation of China (61573132).
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Yang, YH., Shi, Y. Application of Improved BP Neural Network in Information Fusion Kalman Filter. Circuits Syst Signal Process 39, 4890–4902 (2020). https://doi.org/10.1007/s00034-020-01393-y
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DOI: https://doi.org/10.1007/s00034-020-01393-y