Multi-sensor information fusion optimal white noise de-convolution filter based on Kalman filtering method

Using the Kalman filter method and white noise estimation theory,under the linear minimum variance optimal information fusion criterion,the multi-sensor information fusion steady-state optimal white noise de-convolution filter is presented,where the formula for computing covariance matrices between the local filtering error is given,which can be applied to compute the optical fusion weighting coefficient matrices.Compared with the single sensor case,the accuracy of the fused filter is improved.It can be applied to signal processing in oil seismic exploration.A simulation example for three sensors information fusion Bernoulli-Gaussian white noise de-convolution filter shows their effectiveness.