An extension of the split Levinson algorithm and its relatives to the joint process estimation problem

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 est...

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Bibliographic Details
Published in:IEEE transactions on information theory 1989-03, Vol.35 (2), p.482-485
Main Authors: Delsarte, P., Genin, Y.
Format: Article
Language:English
Subjects:
Applied sciences
Arithmetic
Detection, estimation, filtering, equalization, prediction
Economic forecasting
Equations
Exact sciences and technology
Information, signal and communications theory
Lattices
Nonlinear filters
Polynomials
Reflection
Signal and communications theory
Signal processing
Signal, noise
Stochastic processes
Telecommunications and information theory
Wiener filter
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Summary: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.< >
ISSN:0018-9448
1557-9654
DOI:10.1109/18.32146