Weighted Measurement Fusion White Noise Deconvolution Filter with Correlated Noise for Multisensor Stochastic Systems

For the multisensor linear discrete time-invariant stochastic control systems with different measurement matrices and correlated noises, the centralized measurement fusion white noise estimators are presented by the linear minimum variance criterion under the condition that noise input matrix is ful...

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Bibliographic Details
Published inMathematical Problems in Engineering Vol. 2012; no. 2012; pp. 66 - 81-104
Main Authors Xue, Jing-Yan, Ding, Kai-Hui, Sun, Shu-Li, Wang, Xin
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Limiteds 01.01.2012
Hindawi Publishing Corporation
Hindawi Limited
Subjects
Accuracy
Algorithms
Computation
Correlation analysis
Deconvolution
Discrete time systems
Estimators
Gaussian process
Lagrange multiplier
Mathematical models
Mathematical problems
Matrices (mathematics)
Noise
Noise control
Noise measurement
Oil exploration
Optimal control
Seismic phenomena
Sensors
Signal processing
Stochastic processes
Stochastic systems
Time series
White noise
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Summary:For the multisensor linear discrete time-invariant stochastic control systems with different measurement matrices and correlated noises, the centralized measurement fusion white noise estimators are presented by the linear minimum variance criterion under the condition that noise input matrix is full column rank. They have the expensive computing burden due to the high-dimension extended measurement matrix. To reduce the computing burden, the weighted measurement fusion white noise estimators are presented. It is proved that weighted measurement fusion white noise estimators have the same accuracy as the centralized measurement fusion white noise estimators, so it has global optimality. It can be applied to signal processing in oil seismic exploration. A simulation example for Bernoulli-Gaussian white noise deconvolution filter verifies the effectiveness.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1024-123X
1563-5147
DOI:10.1155/2012/257619