Time-varying optimal distributed fusion white noise deconvolution estimator

Abstract

White noise deconvolution has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. Using the Kalman filtering method, the time-varying optimal distributed fusion white noise deconvolution estimator is presented for the multisensor linear discrete time-varying systems. It is derived from the centralized fusion white noise deconvolution estimator so that it is identical to the centralized fuser, i.e., it has the global optimality. It is superior to the existing distributed fusion white noise estimators in the optimality and the complexity of computation. A Monte Carlo simulation for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.

Keywords

multisensor information fusion / distributed fusion / white noise deconvolution / global optimality / Kalman filtering

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61104209, Outstanding Youth Science Foundation of Heilongjiang University under Grant No. JCL201103, and Key Laboratory of Electronics Engineering, College of Heilongjiang Province, under Grant No. DZZD2010-5. The authors wish to thank the reviewers for their constructive comments.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg