Based on the Kalman filtering method and white noise estimation theory

under linear minimum variance information fusion criterion weighted by matrices

a multisensor information fusion white noise deconvolution filter is presented for systems with correlated noises.The formula of computing covariances among filtering errors of sensors is presented

which can be applied to compute the optimal fused weighting matrices.Compared to the single sensor case

the accuracy of fused filtering is improved.It can reduce the on-line computational burden

and is suitable for real time applications.It can be applied to signal processing in oil seismic exploration.A simulation example for 3-sensor information fusion Bernoulli-Gaussian white noise deconvolution filter shows its effectiveness.