Multisensor optimal information fusion white noise deconvolution filter

Using the modern time series analysis method and white noise estimation theory, under the linear minimal variance optimal information fusion criterion, a multisensor information fusion white noise deconvolution filter is presented for systems with correlated noises. The formula of computing covarian...

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Bibliographic Details
Published in2009 International Conference on Mechatronics and Automation pp. 2447 - 2451
Main Authors Wang, Xin, Zhu, Qidan, Wu, Yebin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Analysis of variance
correlated noises
Deconvolution
Estimation theory
Information analysis
Information filtering
Information filters
Lubricating oils
optimal information fusion
reflection seismology
Sensor fusion
Time series analysis
White noise
white noise estimators
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Summary:Using the modern time series analysis method and white noise estimation theory, under the linear minimal variance optimal information fusion criterion, a multisensor information fusion white noise deconvolution filter is presented for systems with correlated noises. The formula of computing covariances among filtering errors of sensors is presented, which can be applied to compute the optimal fused weighting matrices. Compared with the single sensor case, the accuracy of the fused filter is improved. It can be applied to signal processing in oil seismic exploration. A simulation example for information fusion Bernoulli-Gaussian white noise deconvolution filter shows its effectiveness.
ISBN:1424426928
9781424426928
ISSN:2152-7431
2152-744X
DOI:10.1109/ICMA.2009.5245986