多传感器分布式融合白噪声反卷积滤波器
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摘要
基于Kalman滤波方法和白噪声估计理论,在按矩阵加权线性最小方差最优融合准则下,提出了带ARMA有色观测噪声系统的多传感器分布式融合白噪声反卷积滤波器,其中推导出用Lyapunov方程计算最优加权的局部估计误差互协方差公式。与单传感器情形相比,可提高融合估值器精度。它可应用于石油地震勘探信号处理。一个三传感器分布式融合Bernoulli-Gauss白噪声反卷积平滑器的仿真例子说明了其有效性。
Based on the Kalman filtering method and white noise estimation theory, under the linear minimum variance optimal information fusion criterion weighted by matrices,a multisensor distributed fusion optimal white noise deconvolution filter is presented for systems with ARMA colored measurement noise,where the formulas of computing cross-covariances among local estimation errors by Lyapunov equations are derived,which is applied to compute optimal weights.Compared to the single sensor case, the accuracy of fused estimators is improved. It can be applied to signal processing in oil seismic exploration. A simulation example for three-sensor distributed fusion Bernoulli-Gaussian white noise deconvolution smoother shows its effectiveness.
Based on the Kalman filtering method and white noise estimation theory, under the linear minimum variance optimal information fusion criterion weighted by matrices,a multisensor distributed fusion optimal white noise deconvolution filter is presented for systems with ARMA colored measurement noise,where the formulas of computing cross-covariances among local estimation errors by Lyapunov equations are derived,which is applied to compute optimal weights.Compared to the single sensor case, the accuracy of fused estimators is improved. It can be applied to signal processing in oil seismic exploration. A simulation example for three-sensor distributed fusion Bernoulli-Gaussian white noise deconvolution smoother shows its effectiveness.
引文
[1]Mendel J M.White-noise estimators for seismic data processing in oil exploration[J].IEEE Trans.Automatic Control,1977,22(5):694-706.
[2]Mendel J M.Optimal Seismic Deconvolution:an Estimation-based Approach[M].New York:Academic Press,1983:1-103.
[3]Sun Shuli.Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor[J].Automatica,2004,40(8):1447-1453.
[4]Sun Shuli,Deng Zili.Multi-sensor optimal information fusion Kalman filter[J].Automatica,2004,40(6):1017-1023.
[5]邓自立,高媛,李云等.基于Kalman滤波的信息融合白噪声最优反卷积滤波器[J].科学技术与工程,2004,4(3):169-171.
[6]邓自立.卡尔曼滤波与维纳滤波——现代时间序列分析方法[M].哈尔滨:哈尔滨工业大学出版社,2000:1-396.
[7]邓自立.自校正滤波理论及其应用——现代时间序列分析方法[M].哈尔滨:哈尔滨工业大学出版社,2003:1-343.
[2]Mendel J M.Optimal Seismic Deconvolution:an Estimation-based Approach[M].New York:Academic Press,1983:1-103.
[3]Sun Shuli.Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor[J].Automatica,2004,40(8):1447-1453.
[4]Sun Shuli,Deng Zili.Multi-sensor optimal information fusion Kalman filter[J].Automatica,2004,40(6):1017-1023.
[5]邓自立,高媛,李云等.基于Kalman滤波的信息融合白噪声最优反卷积滤波器[J].科学技术与工程,2004,4(3):169-171.
[6]邓自立.卡尔曼滤波与维纳滤波——现代时间序列分析方法[M].哈尔滨:哈尔滨工业大学出版社,2000:1-396.
[7]邓自立.自校正滤波理论及其应用——现代时间序列分析方法[M].哈尔滨:哈尔滨工业大学出版社,2003:1-343.
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