Multisensor optimal information fusion input white noise deconvolution estimators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New approach to information fusion steady-state Kalman filtering
Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
White noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication, and signal processing. Based on the optimal information fusion rules, in the linear minimum variance sense, three distributed optimal 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 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, prediction and smoothing problems, and are applicable to the systems with colored measurement noise. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is presented. A Monte Carlo simulation example for the Bernoulli-Gaussian input white noise shows their effectiveness.