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| 1 | SELF-TUNING WEIGHTED MEASUREMENT FUSION WHITE NOISE DECONVOLUTION ESTIMATOR显示文摘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 method.Substituting 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 presented.By 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 realization.Therefore,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. | Sun Xiaojun Deng Zili | 2010 | Journal of Electronics(China)2010，27，1： | 2 |
| 2 | Self-tuning weighted measurement fusion Kalman filter and its convergence显示文摘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 criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using 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 realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness. | Chenjian RAN,Zili DENG (Department of Automation,Heilongjiang University,Harbin Heilongjiang 150080,China) | 2010 | 控制理论与应用(英文版)2010，8，4： | 2 |
| 3 | Reduced-order steady-state descriptor Kalman fuser weighted by block-diagonal matrices显示文摘 | Deng Zili Gao Yuan Tao Guili | 2008 | Information Fusion2008，，9： | 1 |
| 4 | New apptoach to information fusion steady-state Kalman filtering显示文摘 | Deng Zili Gao Yuan Mao Lin | 2005 | Automatica2005，41，10： | 1 |
| 5 | Multi-sensor optimal information fusion Kalman filter显示文摘 | SUN Shuli Deng Zili | 2004 | Automatica2004，40，6： | 1 |
| 6 | Multi - sensor information fusion optimal Kalman filter显示文摘 | Sun Shuli Deng Zili | 2004 | Automatica2004，40，： | 1 |
| 7 | Multi-sensor optimal information fusion Kalman filter显示文摘 | SUN Shuli DENG Zili | 2004 | Aerospace Science & Technology2004，8，1： | 1 |
| 8 | Descriptor Kalman estimation显示文摘 | Deng Zili Liu Yumei | 1999 | International Journal of System Science1999，30，11： | 1 |
| 9 | Descriptor Wiener state estimators显示文摘 | Deng Zili Xu Yan | 2000 | Automatica2000，36，： | 1 |
| 10 | Robust weighted fusion Kalman filters for multisensor time varying systems with uncertain noise variances显示文摘 | Wenjuan Qi Peng Zhang Zili Deng | 2014 | Signal Processing2014，99，14： | 1 |
| 11 | Decoupled Wiener state fuser for descriptor systems显示文摘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. | Chenjian RAN Zili DENG | 2008 | 控制理论与应用(英文版)2008，6，4： | 1 |
| 12 | Multi-sensor optimal information fusion Kalman filter显示文摘 | Sun Shuli Deng Zili | 2004 | Automatica2004，40，6： | 1 |
| 13 | Multi-sensor optimal information fusion Kalman filter显示文摘 | Sun Shuli Deng Zili | 2004 | Automatica2004，40，6： | 1 |
| 14 | Multi-sensor optimal information fusion Kalman filter 显示文摘 | SUN Shuli DENG Zili | 2004 | Automatica2004，40，6： | 1 |
| 15 | Descriptor Wiener state estimators 显示文摘 | DENG Zili XU Yan | 2000 | Automatica2000，36，11： | 1 |
| 16 | Multi-sensor optimal information fusion Kalman filter显示文摘 | Sun Shuli Deng Zili | 2004 | A utomatica2004，40，6： | 1 |
| 17 | Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems 显示文摘 | DENG Zili ZHANG H S LIU S J | 1996 | Automatica1996，32，2： | 1 |
| 18 | Time-domain approaches to multichannel optimal deconvolution 显示文摘 | DENG Zili | 2000 | Int J of Systems Science2000，31，6： | 1 |
| 19 | Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems显示文摘 | Deng ZiLi Zhang Huanshui | 1996 | Automatica1996，32，2： | 1 |
| 20 | Sequential covariance intersection fusion Kalman filter 显示文摘 | Deng Zili Zhang Peng Qi Wenjuan | 2012 | Information Sciences2012，189，： | 1 |
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