Multisensor Information Fusion White Noise Deconvolution Estimators

摘要

White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. For the linear discrete timevarying stochastic control systems with different local dynamic models, three information fusion white noise deconvolution estimators weighted by matrices, diagonal matrices and scalars are presented. They can handle the input white noise fused filtering, prediction and smoothing problems, and are applicable for the systems with colored measurement noise. They are locallyoptimal, and globally suboptimal. The accuracy of the fusers is higher than that of every local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error crosscovariances is given. The corresponding steady-state information fusion white noise deconvolution fusers are also presented, which can reduce the on-line computational burden. A Monte Carlo simulation example for a system with Bernoulli-Gaussian input white noise shows their effectiveness and performances.