Optimal weighted state fusion white noise deconvolution estimator

Using the Kalman filtering method, a globally optimal weighted state fusion white noise deconvolution estimator is presented for the multisensor linear discrete systems with correlated measurement noises. It is derived from the centralized fusion white noise deconvolution estimator so that it is identical to the centralized fuser, i.e. it has the global optimality. Compared with the existing globally suboptimal distributed fusion white noise estimators, the proposed white noise fuser is given based on the local Kalman predictors, and the computation of complex covariance matrices is avoided. A Monte Carlo simulation for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.