多传感器最优信息融合白噪声反卷积滤波器
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摘要
应用现代时间序列分析方法和白噪声估计理论,基于线性最小方差意义下按标量加权最优信息融合准则,对于带白色和有色观测噪声的多传感器单通道系统,提出了分布式融合白噪声反卷积滤波器.它由局部白噪声反卷积滤波器加权构成.可统一处理融合滤波、平滑和预报问题.给出了计算局部滤波误差互协方差公式,可用于计算最优加权.同单传感器情形相比,可提高融合滤波器精度.它可应用于石油地震勘探信号处理.一个3传感器信息融合Bernou lli-Gaussian白噪声反卷积滤波器的仿真例子说明了其有效性.
Using the modern time series analysis method and white noise estimation theory and based on the linear minimum variance optimal information fusion criterion weighted by scalars,a distributed fusion white noise deconvolution filter is presented for multisensor single channel systems with white and colored measurement noises.The proposed filter consists of weighting local white noise deconvolution filters,which can handle the fused filtering,smoothing,and prediction problems in a unified framework.The formula for computing the cross-covariances among local filtering errors is also given,which is applied to compute the optimal weights.Compared with the single sensor case,the accuracy of the fused filter is improved.It can be applied to signal processing in oil seismic exploration.Finally,a simulation example for information fusion Bernoulli-Gaussian white noise deconvolution filter with three-sensor shows its effectiveness.
Using the modern time series analysis method and white noise estimation theory and based on the linear minimum variance optimal information fusion criterion weighted by scalars,a distributed fusion white noise deconvolution filter is presented for multisensor single channel systems with white and colored measurement noises.The proposed filter consists of weighting local white noise deconvolution filters,which can handle the fused filtering,smoothing,and prediction problems in a unified framework.The formula for computing the cross-covariances among local filtering errors is also given,which is applied to compute the optimal weights.Compared with the single sensor case,the accuracy of the fused filter is improved.It can be applied to signal processing in oil seismic exploration.Finally,a simulation example for information fusion Bernoulli-Gaussian white noise deconvolution filter with three-sensor shows its effectiveness.
引文
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[4]SUN Shu li.Mu lti-sensor information fusion wh ite noise filterweighted by scalars based on Kalman pred ictor[J].Au toma tica,2004,40(8):1447-1453.
[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):170-173.(DENG Z ili,GAO Yuan,LI Yun,et al.Information fusion wh itenoise optimal deconvolution filter based on Kalman filtering[J].Science Techn iques and Engineering,2004,4(3):170-173.)
[6]SUN Shu li,D eng Z ili.Mu lti-sensor optimal information fusion Kal-man filter[J].Au toma tica,2004,40(6):1017-1023.
[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,2003:1-342.(DENG Z ili.Self-tuning F ilterining Theory and Applica tions—M odern Tim e Series AnalysisM ethod[M].Harb in:Press ofHarb inInstitute of Technology,2003:1-342.)
[2]MENDEL JM.W h ite-noise estimators for seism ic data processingin oil exploration[J].IEEE Trans on Au toma tic Con trol,1977,AC-22(5):694-706.
[3]DENG Z L,ZHANG H S,LIU S J,et al.Optimal and self-tun ingwh ite noise estimators w ith applications to deconvolution and filte-ring prob lem s[J].Au toma tica,1996,32(2):199-216.
[4]SUN Shu li.Mu lti-sensor information fusion wh ite noise filterweighted by scalars based on Kalman pred ictor[J].Au toma tica,2004,40(8):1447-1453.
[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):170-173.(DENG Z ili,GAO Yuan,LI Yun,et al.Information fusion wh itenoise optimal deconvolution filter based on Kalman filtering[J].Science Techn iques and Engineering,2004,4(3):170-173.)
[6]SUN Shu li,D eng Z ili.Mu lti-sensor optimal information fusion Kal-man filter[J].Au toma tica,2004,40(6):1017-1023.
[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,2003:1-342.(DENG Z ili.Self-tuning F ilterining Theory and Applica tions—M odern Tim e Series AnalysisM ethod[M].Harb in:Press ofHarb inInstitute of Technology,2003:1-342.)
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