Title :
Multisensor Information Fusion White Noise Estimator
Author :
Wang, Xin ; Li, Yun ; Deng, Zili
Author_Institution :
Dept. of Autom., Heilongjiang Univ., Harbin
Abstract :
Using the modern time series analysis method and white noise estimation theory, under the linear minimum variance optimal information fusion criterion, for multisensor systems with colored measurement noise, the multisensor optimal information fusion white noise deconvolution Wiener filter is presented, which consists of weighting local white noise deconvolution Wiener filters. The formula of computing covariances among the local filtering errors is presented, and the formula of optimal weighting coefficients is also given. 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. A simulation example for information fusion Bernoulli-Gaussian white noise deconvolution filter shows its effectiveness
Keywords :
Wiener filters; covariance analysis; deconvolution; geophysical signal processing; sensor fusion; time series; white noise; Bernoulli-Gaussian white noise; colored measurement noise; deconvolution Wiener filter; fused filter; linear minimum variance; local filtering errors; local white noise; multisensor information fusion; multisensor optimal information fusion; oil seismic exploration; optimal information fusion criterion; signal processing; time series analysis; white noise estimation theory; white noise estimator; Analysis of variance; Deconvolution; Estimation theory; Information analysis; Lubricating oils; Multisensor systems; Noise measurement; Time series analysis; White noise; Wiener filter; Wiener filter; deconvolution; multisensor information fusion; optimal weighting fusion; reflection seismology; white noise estimators;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712598