统一和通用的白噪声信息融合反卷积估值器
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摘要
对于带不同局部动态模型的多传感器线性离散时变随机控制系统,应用Kalman滤波方法,在按标量加权最优融合准则下,提出了统一和通用的最优信息融合白噪声反卷积估值器,并对定常系统提出了稳态最优信息融合白噪声反卷积估值器。它们可统一处理白噪声反卷积融合滤波、平滑和预报问题。为了计算最优加权,提出了输入白噪声局部估计误差互协方差计算公式。它们在石油、地震勘探领域中有重要的应用背景。
For the multisensor linear discrete time-varying stochastic control systems with the different local dynamic models, using the Kalman filtering method, under the optimal fusion criterion weighted by scalars, the unified and universal optimal information fusion white noise deconvolution estimators are presenced, and for the corresponding time-invariant systems, the steady-state optimal information fusion white noise deconvolution estimators are also presented. They can handle the white noise deconvolution fused filtering, smoothing and prediction problems in a unified framework. In order to compute the optimal weights, the formula of computing the cross-covariances among local estimation errors of input white noise. They have an important applied background in oil seismic exploration.
For the multisensor linear discrete time-varying stochastic control systems with the different local dynamic models, using the Kalman filtering method, under the optimal fusion criterion weighted by scalars, the unified and universal optimal information fusion white noise deconvolution estimators are presenced, and for the corresponding time-invariant systems, the steady-state optimal information fusion white noise deconvolution estimators are also presented. They can handle the white noise deconvolution fused filtering, smoothing and prediction problems in a unified framework. In order to compute the optimal weights, the formula of computing the cross-covariances among local estimation errors of input white noise. They have an important applied background in oil seismic exploration.
引文
1Mendel J M.White-noise estimators for seismic data processing inoil exploration.IEEE Transactions Automatic Control,1977;22(5):694—706
2Mendel J M.Optimal seismic deconvolution:an estimation-based approach.New York:Academic Press,1983
3Sun Shuli.Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor.Automatica,2004;40(7):1447—1453
4邓自立,王欣,李云.多传感器最优信息融合白噪声反卷积滤波器,电子学报,2005;33(5):860—863
5邓自立,崔荣信.多传感器全局最优观测融合白噪声反卷积滤波器.科学技术与工程,2005;5(5):267—270
6邓自立,高媛,李云,白敬刚,崔崇信.基于Kalman滤波的信息融合白噪声最优反卷积滤波器.科学技术与工程,2004;4(3):169—171
7邓自立.最优估计理论及其应用——建模、滤波、信息融合估计.哈尔滨:哈尔滨工业大学出版社,2005
8Deng Zili,Gao Yuan,Mao Lin,Li Yun,Hao Gang.New approach to information fusion steady-state Kalman filtering.Automatica,2005;41(10):1695—1707
2Mendel J M.Optimal seismic deconvolution:an estimation-based approach.New York:Academic Press,1983
3Sun Shuli.Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor.Automatica,2004;40(7):1447—1453
4邓自立,王欣,李云.多传感器最优信息融合白噪声反卷积滤波器,电子学报,2005;33(5):860—863
5邓自立,崔荣信.多传感器全局最优观测融合白噪声反卷积滤波器.科学技术与工程,2005;5(5):267—270
6邓自立,高媛,李云,白敬刚,崔崇信.基于Kalman滤波的信息融合白噪声最优反卷积滤波器.科学技术与工程,2004;4(3):169—171
7邓自立.最优估计理论及其应用——建模、滤波、信息融合估计.哈尔滨:哈尔滨工业大学出版社,2005
8Deng Zili,Gao Yuan,Mao Lin,Li Yun,Hao Gang.New approach to information fusion steady-state Kalman filtering.Automatica,2005;41(10):1695—1707
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