Information fusion white noise deconvolution estimators for time-varying systems

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. Based on the Kalman filtering method and the optimal information fusion rules in the linear minimum variance sense, three distributed fused white noise deconvolution estimators weighted by matrices, diagonal matrices, and scalars, are presented for the linear discrete time-varying stochastic systems with multisensor and with different local dynamic models, respectively. 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, and are applicable to the multisensor systems with colored measurement noises. In order to compute the optimal weights, the new formula of computing the local estimation error cross-covariances is presented, and the steady-state white noise deconvolution fusers are also presented, which can reduce the on-line computational burden. Two Monte Carlo simulation examples for the Bernoulli–Gaussian input white noise show their effectiveness.