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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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Self-tuning weighted measurement fusion Kalman filter and its convergence

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控制理论与应用（英文版） 2010年第4期8卷 435-440页

作者： Chenjian rAN,zili deng (department of automation,heilongjiang university,harbin heilongjiang 150080,china) department of automation Heilongjiang University Harbin Heilongjiang China

For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion *** these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is *** the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a ***,the self-tuning weighted measurement fusion Kalman filter has asymptotic global *** simulation example for a 4-sensor target tracking system verifies its effectiveness.

关键词： Multisensor weighted measurement fusion Fused parameter estimator Fused noise variance estimator Self-tuning fusion Kalman filter Asymptotic global optimality Convergence

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维普期刊数据库

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A new information fusion white noise deconvolution estimator

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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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维普期刊数据库

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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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Self-tuning White Noise Deconvolution Fuser with Asymptotic Global Optimality

Self-tuning White Noise Deconvolution Fuser with Asymptotic ...

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2010 Chinese Control and Decision Conference

作者： xiaojun sun,zili deng department of automation,heilongjiang university,harbin,150080,china

＜正＞White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration,communication,signal processing,and state *** the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is given by the correlation *** the Kalman filtering method,based on the self-tuning riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is *** the dynamic error system analysis（DESA） method,it is proved that the self-tuning white noise deconvolution fuser converges to the steady-state optimal white noise deconvolution fuser in a realization so that it has the asymptotic global optimality.A simulation example for a 3-sensor system with Bernoulli-Gaussian input white noise shows its effectiveness.

关键词： Multisensor Information Fusion Weighted Measurement Fusion Self-tuning Fuser White Noise Deconvolution Global Optimality Kalman Filtering Convergence

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Self-tuning Measurement Fusion Kalman Filter with Correlated Measurement Noises and Its convergence

Self-tuning Measurement Fusion Kalman Filter with Correlated...

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第二十七届中国控制会议

作者： Gao Yuan,deng zili department of automation,heilongjiang university,harbin 150080,p.r.china

For the multisensor system with unknown noise statistics and correlated measurement noises,based on the solution of the matrix equations for correlation function,the on-line estimators of the noise variances and cross-covariances are obtained. Further,a self-tuning weighted measurement fusion Kalman filter is *** on the stability of the dynamic error system and the concept of the convergence in a realization,it is strictly proved that the proposed self-tuning filter convergences to the steady-state optimal Kalman filter in a realization or with probability one,so that it has asymptotic global optimality. Compared with the centralized self-tuning Kalman filter,it can reduce the computational burden,and is suitable for real time applications.A simulation example for a target tracking system with 3-sensor shows its effectiveness.

关键词： Multisensor measurement fusion Self-tuning Kalman filter Convergence in a realization Correlation method

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Self-tuning Weighted Measurement Fusion Wiener Signal Filter

Self-tuning Weighted Measurement Fusion Wiener Signal Filter

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第二十九届中国控制会议

作者： GAO Yuan,deng zili department of automation,heilongjiang university,harbin 150080,p.r.china

For the multisensor single channel autoregressive moving average（ArMA）signals with unknown model parameters and noise variances,using the recursive instrumental variable（rIV）and the correlated method,the strong consistent information fusion estimators of model parameters and noise variances are presented,and then substituting them into the optimal weighted measurement fusion Wiener signal filter,a self-tuning weighted measurement fusion Wiener signal filter is ***,applying the dynamic error system analysis（DESA）method,it is rigorously proved that the self-tuning fused Wiener filter converges to the optimal fused Wiener filter in a realization,so that it has asymptotically global optimality.A simulation example shows its effectiveness.

关键词： Self-tuning Weighted Measurement Fusion Filter Correlation Method Information Fusion Identifier Unknown Model parameters Convergence in a realization

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Self-tuning Measurement Fusion Kalman Filter for Multisensor Systems with Companion Form and Common Disturbance Noise

Self-tuning Measurement Fusion Kalman Filter for Multisensor...

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第二十九届中国控制会议

作者： rAN Chenjian,deng zili department of automation,heilongjiang university,harbin 150080,p.r.china

For the multisensor systems with companion form and common disturbance noise,when the model parameters and noise variances are all unknown,using recursive extended least squares（rELS）algorithm,the local and fused estimators of part model parameters are *** then,the information fusion noise variance estimators and other parameters estimators are presented by using correlation method and the Gevers-Wouters algorithm with a dead *** have strong ***,a self-tuning weighted measurement fusion Kalman filter based on a self-tuning riccati equation is *** the the dynamic error system analysis（DESA）method,it is rigorously proved that the self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter in a realization,so that it has asymptotic global *** self-tuning filter can be applied to the signal processing to obtain the self-tuning measurement fusion signal filters.A simulation example of a self-tuning fused filter for ArMA signal with 3-sensor signal shows its effectiveness.

关键词： Multisensor Information Fusion Filter Measurement Fusion Common Noises Self-tuning Fusion Filter parameter Identification

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Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

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Journal of Systems Engineering and Electronics 2010年第4期21卷 666-674页

作者： xiaojun sun zili deng department of automation Heilongjiang University Harbin 150080 R R. China

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation *** it into the steady-state riccati equation,the self-tuning riccati equation is *** the Kalman filtering method,based on the self-tuning riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is *** the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

关键词： multisensor information fusion measurement fusion self-tuning fuser white noise deconvolution asymptotic global optimality Kalman filtering convergence.

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维普期刊数据库

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Optimal Fusion Kalman Filter for Systems with Colored Measurement Noises

Optimal Fusion Kalman Filter for Systems with Colored Measur...

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第五届全球智能控制与自动化大会会议

作者： Shuli sun zili deng department of automation university of heilongjiang harbin 150080 heilongjiang province china

＜正＞ The optimality of the optimal fusion criterion inthe linear minimum variance sense is further *** onthis optimal fusion criterion,an optimal fusion Kalman filter ispresented for discrete time-varying linear stochastic controlsystems measured by multiple sensors with colored measurementnoises,which is equivalent to an optimal fusion Kalman predictorfor systems measured by multiple sensors with correlated *** has a two-layer fusion structure with a fault tolerant ***,it has higher precision than any local filter *** with the centralized filter,it has better reliability *** it into a radar tracking system with threesensors shows its effectiveness.

关键词： multisensor optimal information fusion criterion optimal fusion Kalman filter fault tolerance

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