Fast minimum variance deconvolution

We propose a statistical estimation approach to the deconvolution problem which is optimal in the minimum variance sense when the a priori knowledge on the signal to be restored is strictly limited to its first two moments. By viewing the estimation problem as a degenerate case of a Kalman filter applied to a static system with time-varying measurements and by developing the corresponding Chandrasekhar-type equations, the estimator may be obtained recursively and implemented with a fast practical algorithm.