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

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 MEASUREMENT FUSION KALMAN FILTER WITH CORRELATED MEASUREMENT NOISES

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Journal of Electronics(China) 2009年第5期26卷 614-622页

作者： Gao Yuan Ran Chenjian Deng Zili Department of Automation Heilongjiang University Harbin 150080 China

For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.

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

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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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