Multisensor Information Fusion White Noise Deconvolution Smoother

White noise deconvolution or input white noise estimation problem has important application background in oil seismic exploration. For the linear discrete time-varying stochastic control systems with multisensor and colored measurement noises, using the Kalman filtering method, under the optimal fusion weighted by matrices, diagonal matrices and scalars, optimal information fusion white noise deconvolution estimators are presented, and for the corresponding time-invariant systems, the steady-state optimal information fusion white noise deconvolution estimators are also given. The accuracy of the fuser with the matrix weights is higher than that of the fuser with scalar weights, but its computational burden is larger than that of the fuser with scalar weights. The accuracy and computational burden of the fuser with diagonal matrix weights are between both of them. They are locally optimal, and globally suboptimal. The accuracy of the fusers is higher than that of each local white noise estimator. They can handle the white noise fused filtering, smoothing and prediction problems. In order to compute the optimal weights, the White noise deconvolution or input white noise estimation problem has important application background in oil seismic exploration. For the linear discrete time-varying stochastic control systems with multisensor and colored measurement noises, using the Kalman Altering method, under the optimal fusion weighted by matrices, diagonal matrices and scalars, optimal information fusion white noise deconvolution estimators are presented, and for the corresponding time-invariant systems, the steady-state optimal information fusion white noise deconvolution estimators are also given. The accuracy of the fuser with the matrix weights is higher than that of the fuser with scalar weights, but its computational burden is larger than that of the fuser with scalar weights. The accuracy and computational burden of the fuser with diagonal matrix weights are between- both of them. They are locally optimal, and globally suboptimal. The accuracy of the fusers is higher than that of each local white noise estimator. They can handle the white noise fused filtering, smoothing and prediction problems. In order to compute the optimal weights, the new formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for a Bernoulli-Gaussian input white noise shows the effectiveness and performances of the proposed white noise fusers. new formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for a Bernoulli-Gaussian input white noise shows the effectiveness and performances of the proposed white noise fusers.