Globally optimal distributed state fusion white noise deconvolution estimators

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. The globally optimal distributed state fusion white noise deconvolution estimators are presented for the multisensor linear discrete systems using the Kalman filtering method. They are derived from the centralized fusion white noise deconvolution estimators so that they are identical to the centralized fusers, i.e. they have the global optimality. Compared with the existing globally suboptimal distributed state fusion white noise estimators, the computation of complex covariance matrices is avoided. A simulation for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.