Weighted fusion steady-state white noise deconvolution estimators

White noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. Under the linear minimum variance optimal weighted fusion rules, the local and weighted fusion steady-state white noise deconvolution estimators are presented by the Kalman filtering method for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and is applicable to the systems with colored measurement noise. The fused estimators are locally optimal and globally suboptimal. The accuracy of the fusers is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows their effectiveness and performances.