A fixed-point smoothing algorithm in discrete-time systems with correlated signal and noise

The linear least mean-square fixed-point smoothing problem in discrete-time systems is formulated in the general case where the signal is any nonstationary stochastic process of second order which is observed in the presence of an additive white noise correlated with the signal. Under the only assumption that the correlation functions involved are factorizable kernels, an efficient recursive computational algorithm for the fixed-point smoother is designed. Also, a filtering algorithm is devised.