Abstract
For the multichannel autoregressive moving average (ARMA) signals with unknown model parameters and noise variances, the online information fusion estimators of model parameters and noise variances can be obtained, based on the system identification and correlation method. Then, by substituting them into the optimal fusion signal Kalman filter , a self-tuning fusion Kalman filter for the multichannel multisensor ARMA signal is presented. By applying the dynamic error system analysis (DESA) method, it is proven that the proposed self-tuning fusion signal Kalman filter converges to the optimal fusion signal Kalman filter in a realization, so that it obtains asymptotic optimality. A simulation example shows its effectiveness.
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Acknowledgment
This work was 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 Information Fusion Kalman Filter for Multichannel ARMA Signals. 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_19
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DOI: https://doi.org/10.1007/978-1-4419-8849-2_19
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