Abstract
For the multisensor multichannel autoregressive (AR) signals with unknown model parameters and noise variances, the estimators of model parameters and noise variances can be obtained based on the multidimensional recursive extended least squares (RELS) algorithm and the correlation method. Furthermore, a self-tuning information fusion Wiener filter is presented based on the modern time series analysis method by substituting the estimators for the true values. One simulation example shows the consistence of the estimators of the model parameters and noise variances and the convergence of the self-tuning Wiener filter.
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Acknowledgment
The authors thank for support from the National Natural Science Foundation of China (60874063), Automatic Control Key Laboratory of Heilongjiang University, Support Program for Young Professionals in Regular Higher Education Institutions of Heilongjiang Province (1155G13), and Science and Technology Research Foundation of Heilongjiang Education Department (11553101).
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Liu, J., Deng, Z. (2012). Self-tuning Information Fusion Wiener Filter for Multisensor Multichannel AR 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_20
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DOI: https://doi.org/10.1007/978-1-4419-8849-2_20
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