An extension of the split Levinson algorithm and its relatives to the joint process estimation problem
P. Delsarte, Yves V. Genin · IEEE Transactions on Information Theory · 1989
It is shown that the split Levinson algorithm, the split Schur algorithm, and the split lattice algorithm to compute the reflection coefficients of the optimal linear prediction filter for a discrete-time stationary stochastic process can be extended to the more general case of the joint process estimation problem. The new algorithms are essentially based on well-defined recurrence relations for symmetric prediction filters and symmetric estimation filters. They are more economical than the standard methods in terms of storage space and number of arithmetic operations.>