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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 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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self-tuning decoupled fusion Kalman filter based on the Riccati equation

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Frontiers of Electrical and Electronic Engineering in China 2008年第4期3卷 459-464页

作者： Xiaojun SUN Peng ZHANG Zili DENG Department of Automation Heilongjiang UniversityHarbin 150080China

An online noise variance estimator for multi-sensor systems with unknown noise variances is proposed by using the correlation *** on the Riccati equa-tion and optimal fusion rule weighted by scalars for state components,a self-tuning component decoupled informa-tion fusion Kalman filter is *** is proved that the filter converges to the optimal fusion Kalman filter in a realization by dynamic error system analysis method,so that it has asymptotic *** effectiveness is demon-strated by simulation for a tracking system with 3 sensors.

关键词： multi-sensor information fusion decoupled fusion self-tuning fuser Kalman filter convergence in a realization

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self-tuning WEIGHTED MEASUREMENT FUSION WHITE NOISE DECONVOLUTION ESTIMATOR

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Journal of Electronics(China) 2010年第1期27卷 51-59页

作者： Sun Xiaojun Deng Zili Department of Automation Heilongjiang University Harbin 150080 China

For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics,an on-line noise statistics estimator is obtained using the correlation *** it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering,a self-tuning weighted measurement fusion white noise deconvolution estimator is *** the Dynamic Error System Analysis(DESA) method,it proved that the self-tuning fusion white noise deconvolution estimator converges to the steady-state optimal fusion white noise deconvolution estimator in a ***,it has the asymptotically global optimality.A simulation example for the tracking system with 3 sensors and the Bernoulli-Gaussian input white noise shows its effectiveness.

关键词： Multi-sensor information fusion self-tuning fuser White noise deconvolution Global optimality Convergence

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维普期刊数据库

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self-tuning Measurement Fusion White Noise Deconvolution Estimator

Self-tuning Measurement Fusion White Noise Deconvolution Est...

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2009中国控制与决策会议

作者： Xiaojun Sun,Zili Deng Department of Automation,Heilonejiang University,Harbin 150080,China

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 measurement noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation *** on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented using the Kalman filtering *** is proved that the self-tuning fusion white noise deconovlution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by the dynamic error system analysis(DESA) method,so that it has the asymptotic global optimality.A simulation example for a tracking system with 3 sensors shows its effectiveness.

关键词： Multisensor Information Fusion Weighted Measurement Fusion self-tuning fuser White Noise Deconvolution Asymptotic Global Optimality Kalman Filtering Convergence

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self-tuning Measurement Fusion Wiener Filter for Autoregressive Signals

Self-tuning Measurement Fusion Wiener Filter for Autoregress...

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2010 Chinese Control and Decision Conference

作者： Yuan Gao,Zili Deng Department of Automation,Heilongjiang University,Harbin 150086,China

＜正＞For the autoregressive（AR） signals with multisensor,unknown model parameters and unknown noise variances,using the recursive extended least square（RELS） and the correlation method,the strong consistent information fusion estimators of model parameters and noise variances are presented,and then by substituting them into the optimal weighted measurement fusion Wiener filter based on the autoregressive moving average（ARMA） innovation model,a self-tuning weighted measurement fusion Wiener signal filter is ***,applying the dynamic error system analysis（DESA） method,it is 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： Measurement Fusion Wiener signal filter Unknown Model Parameters Strong Consistent Estimators self-tuning fuser Dynamic Error System Analysis(DESA) Method

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