Optimal White Noise Deconvolution Filter, Multi-sensor Information Fusion Based On Kalman Filtering Method

White noise deconvolution problem has important application backgrounds in oil seismic exploration, communication, and signal processing. Using Kalman filtering method and white noise estimation theory, based on the Riccati equation, under linear minimum variance information fusion criterion, for the multisensor systems with white and coloured measurement noises in the state space model, the weighting distributed information fusion optimal white noise deconvolution filters are derived, and for the multisensor single channel systems with colored measurement noise in the transfer function model, applying the transfer of the transfer function model to the state space model, by augmenting state method ,the distributed information fusion optimal white noise deconvolution filters are presented. The formula of computing cross-covariances among filtering errors of local sensors is presented, which can be applied to compute the optimal fused weights. Compared to the single sensor case, the accuracy of fused filtering is improved. It can be applied to signal processing in oil seismic exploration. Many simulation examples for Bernoulli-Gaussian white noise distributed fusion deconvolution filter show their effectiveness of the proposed algorithms.