Globally Optimal Weighted Measurement fusion white noise deconvolution estimator for time-varying systems

White noise deconvolution or input white noise estimation problem has important application background in oil seismic exploration. For the linear discrete time-varying stochastic systems with multisensor and uncorrelated noises, a globally optimal weighted measurement fusion white noise deconvolution smoother is presented based on the method of weighted least squares, using Kalman filtering method, which can handle the white noise fusion filtering, smoothing and prediction problems in a unified framework. A Monte Carlo simulation example for a Bernoulli-Gaussian input white noise fused smoother shows its effectiveness.