Comment on "An innovations approach to least-squares estimation, part I: Linear filtering in additive white noise"

The innovations approach to linear least-squares aIF proximation problems is ûrst to "whiten' the observed data by a causal and invertible operation, ând then to treat the resulting simpler white-noise observations problem. This technique was successfully used by Bode and Shannon to obtain a simple derivation of the classical'Wiener ûltering problem for stationary processes over & semi-inûnite interval. Ilere we shall extend the technique to handle nonstationary conlinuous-time processes over ûnite intervals. In Part I we shall apply this method to obtain a simple derivation of the Kalman-Bucy recursive ûltering formulas (for both continuous-time and discrete-time processes) and also some minor generalizations thereof.