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.

Key words： multisensor information fusion; distributed fusion; white noise deconvolution; global optimality; Kalman filtering

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.

References

1 Mendel J M. White-noise estimators for seismic data processing in oil exploration. IEEE Transactions on Automatic Control, 1977, 22(5): 694-706

2 Mendel J M, Kormylo J. New fast optimal white-noise estimators for deconvolution. IEEE Transactions on Geoscience Electronics, 1977, 15(1): 32-41

3 Mendel J M. Optimal Seismic Deconvolution: An Estimation-Based Approach. New York: Academic Press, 1983

4 Deng Z L, Zhang H S, Liu S L, Zhou L. Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems. Automatica, 1996, 32(2): 199-216

5 Deng Z L. Unifying and universal optimal white noise estimators for time-varying systems. Control Theory & Applications, 2003, 20(1): 143-146 (in Chinese)

6 Reocker J A, McGillem C D. Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 447-449

7 Gan Q, Harris C J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(1): 273-279

8 Deng Z L, Gao Y, Mao L, Li Y, Hao G. New approach to information fusion steady-state Kalman filtering. Automatica, 2005, 41(10): 1695-1707

9 Sun S L. Multi-sensor information fusion white noise filter weighted by scalars based Kaman predictor. Automatica, 2004, 40(8): 1447-1453

10 Sun X J, Gao Y, Deng Z L. Information fusion white noise deconvolution estimators for time-varying systems. Signal Processing, 2008, 88(5): 1233-1247

11 Sun X J, Gao Y, Deng Z L, Li C, Wang J W. Multi-model information fusion Kalman filtering and white noise deconvolution. Information Fusion, 2010, 11(2): 163-173

12 Anderson B D O, Moore J B. Optimal Filtering Englewood Cliffs. New Jersey: Prentice-Hall, 1979

13 Deng Z L. Optimal Estimation Theory with Applications - Modeling, Filtering, and Information Fusion Estimation. Harbin: Harbin Institute of Technology Press, 2005 (in Chinese)