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A new information fusion white noise deconvolution estimator

控制理论与应用（英文版） 2009年第4期7卷 438-444页

作者： Xiaojun SUN Shigang WANG Zili DENG Department of Automation University of Heilongjiang Harbin Heilongjiang 150080 China

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, a new information fusion white noise deconvolution estimator is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and it is applicable to systems with colored measurement noises. It is locally optimal, and is globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows the effectiveness and performances.

关键词： Multisensor information fusion Weighted fusion white noise estimator Deconvolution Modern time series analysis method

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Decoupled Wiener state fuser for descriptor systems

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控制理论与应用（英文版） 2008年第4期6卷 365-371页

作者： Chenjian RAN Zili DENG Department of Automation Heilongjiang University Harbin Heilongjiang 150080 China

By the modem time series analysis method, based on the autoregressive moving average （ARMA） innovation models and white noise estimation theory, using the optimal fusion rule weighted by diagonal matrices, a distributed descriptor Wiener state fuser is presented by weighting the local Wiener state estimators for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented based on cross-covariances among the local innovation processes, input white noise, and measurement white noises. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness and correctness.

关键词： Multisensor information fusion Weighted fusion Decoupled fusion Descriptor system Wiener statefuser white noise estimator ARMA innovation model Modern time series analysis method

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Distributed fusion white noise deconvolution estimators

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Frontiers of Electrical and Electronic Engineering in China 2009年第3期4卷 270-277页

作者： Xiaojun SUN Zili DENG Department of Automation University of HeilongjiangHarbin 150080China

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration,communication and signal *** combining the Kalman filtering method with the modern time series analysis method,based on the autoregressive moving average(ARMA)innovation model,new distributed fusion white noise deconvolution estimators are presented by weighting local input white noise estimators for general multisensor systems with different local dynamic models and correlated *** new estimators can handle input white noise fused filtering,prediction and smoothing problems,and are applicable to systems with colored measurement *** accuracy is higher than that of local white noise deconvolution *** compute the optimal weights,the new formula for local estimation error cross-covariances is given.A Monte Carlo simulation for the system with Bernoulli-Gaussian input white noise shows their effectiveness and performance.

关键词： multisensor information fusion deconvolution white noise estimator seismology modern time series analysis method Kalman filtering method

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Information Fusion Steady-State white noise Deconvolution estimators with Time-Delayed Measurements

Information Fusion Steady-State White Noise Deconvolution Es...

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第二十七届中国控制会议

作者： Sun Xiaojun,Deng Zili Department of Automation,University of Heilongjiang,Harbin 150080,P.R.China

white noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration,communication and signal *** the modern time series analysis method,based on the autoregressive moving average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and correlated *** can handle the input white noise fused filtering,prediction and smoothing problems,and are applicable for the multisensor systems with colored measurement *** are locally optimal,and globally *** accuracy of the fusers is higher than that of each local white noise *** order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.

关键词： Multisensor Information Fusion white noise estimator Deconvolution Time-delayed Measurements Modern Time Series Analysis Method

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Information Fusion Steady-state white noise Deconvolution estimators

Information Fusion Steady-state White Noise Deconvolution Es...

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第二十七届中国控制会议

作者： Sun Xiaojun,Wang Shigang,Deng Zili Department of Automation,University of Heilongjiang,Harbin 150080,P.R.China

white noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration,communication and signal *** the modern time series analysis method,based on the autoregressive moving average（ARMA） innovation model and the optimal fusion rules in linear minimum variance sense,the new information fusion white noise deconvolution estimators are presented for the general multisensor systems with different local dynamic models and correlated noises,*** can handle the input white noise fused filtering,prediction and smoothing problems,and are applicable for the systems with colored measurement *** is locally optimal and globally *** accuracy of the fuser is higher than that of each local white noise *** order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows their effectiveness and performances.

关键词： Information fusion Different local model white noise estimator Deconvolution Weighted fusion

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Descriptor Wiener state fuser

Descriptor Wiener state fuser

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2009中国控制与决策会议

作者： Chenjian Ran~1,Zili Deng~2 1.Department of Automation,Heilongjiang University,Harbin 150080 2.Department of Automation,Heilongjiang University,Harbin 150080

＜正＞By the modem time series analysis method,based on the autoregressive moving average（ARMA） innovation model and white noise estimation theory,a new local descriptor Wiener state estimators are presented,and using the optimal fusion rule weighted by diagonal matrices,a distributed decoupled descriptor Wiener state fuser is presented for the linear discrete stochastic descriptor systems with *** realizes a decoupled fusion estimation for state *** descriptor Wiener state fuser is obtained by weighting the local Wiener state *** order to compute the optimal weights,the formulas of computing the cross-covariances among local estimation errors are *** can handle the fused filtering,smoothing,and prediction problems in a unified *** accuracy is higher than that of each local estimator.A Monte Carlo simulation example shows its effectiveness.

关键词： Multisensor information fusion Weighted fusion Decoupled fusion Descriptor system Wiener state fuser white noise estimator Modern time series analysis method and ARMA innovation model

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Information Fusion white noise Deconvolution estimators for Multisensor Systems with Different Local Models

Information Fusion White Noise Deconvolution Estimators for ...

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2009中国控制与决策会议

作者： Xiao-Jun Sun,Zi-Li Deng Department of Automation,University of Heilongjiang,Harbin 150080,China

＜正＞white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration,communication,and signal *** on the optimal information fusion rules,in the linear minimum variance sense,three distributed optimal fused white noise deconvolution estimators weighted by matrices, diagonal matrices and scalars,are presented for the linear discrete time-varying stochastic systems with multisensor and different local dynamic models,*** accuracy of the fusers is higher than that of each local white noise *** can handle the white noise fused filtering,prediction and smoothing problems,and are applicable to the systems with colored measurement *** order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is presented.A Monte Carlo simulation example for the Bernoulli-Gaussian input white noise shows their effectiveness.

关键词： Multisensor Information Fusion Time-varying System Deconvolution white noise estimator Kalman Filtering Method

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