Abstract
For the multisensor systems with unknown model parameters and noise variances, using the system identification method, the online fusion estimators of the model parameters and the noise variances can be obtained, and then substituting them into a centralized fusion optimal information filter, a self-tuning centralized fusion information filter is presented. Compared with the centralized fusion optimal Kalman filter based on the Riccati equation, it can reduce the computational burden. Using the dynamic error system analysis (DESA) method, it is proven that the self-tuning centralized fusion information filter converges with the centralized fusion optimal information filter, so that it has asymptotic global optimality . A simulation example applied to the signal processing shows its effectiveness.
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References
Gan Q., Harris C.J.: Comparision of two measurement fusion methods for Kalman-filter-based multisensor data fusion. In: IEEE Trans. Aerospace and Electronic Systems, vol. 37, no. 1, pp. 273–279, (2001)
Kailath T., Sayed A. H., Hassibi B.: Linear Estimation. Upper Saddle River: Prentice-Hall, (2000)
Hagander P., Wittenmark B.: A self-tuning filter for fixed-lag smoothing. In: IEEE Trans. Information Theory, vol. 23, no. 3, pp. 377–384, (1977)
Guan X.H., Deng Z.L., Shi Y.: Self-tuning centralized fusion information filter. In: Science Technology and Engineering, vol. 10, no. 2, pp. 372–376, (2010)
Ran C.J, Gu L., Deng Z.L.: Self-tuning Centralized Fusion Kalman Filter for Multisensor Systems with Companion Form and its Convergence. In: 2010 8th IEEE International Conference on Control and Automation, pp. 645–650. Xiamen, (2010)
Zhu Y.m., You Z.S., Zhao J., et al. The optimality for the distributed Kalman filtering fusion with feedback. In: Automatica, vol. 37, pp. 1489–1493, (2001)
Ljung L.: System Identification: Theory for the User (Second Edition). Beijing:Tsinghua University Press, Prentice Hall, (1999)
Tao G.L., Deng Z.L.: Convergence of the self-tuning Riccati equaiton for systems with unknown parameters and noise variances. In: Proceedings of the 8th World Congress on Intelligent Control and Automation, pp. 5732–5736. Jinan, (2010)
Xu H.Q., Deng Z.L., Zhang M.B.: Multidimensional and Multiple Recursive Instrumental Variable Identification Algorithms. In: Science Technology and Engineering, vol. 10, no. 2, pp. 366–371, (2010)
Gevers M., Wouters W.R.E.: In: An innovation approach todiscrete-time stochastic realization problem. Quartely Journal of Automatic Control, vol. 19, no. 2, pp. 90–110, (1978)
Acknowledgment
This work is supported by the National Natural Science Foundation of China under grant NSFC-60874063, the Science and Technology Research Foundation of Heilongjiang Education Administrator under grant 11553101, and the Automatic Control Key Laboratory of Heilongjiang University.
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Tao, G., Deng, Z. (2012). Self-tuning Centralized Fusion Information Filter with Unknown Parameters and its Convergence. In: Chen, R. (eds) 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8849-2_18
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DOI: https://doi.org/10.1007/978-1-4419-8849-2_18
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