Least squares order-recursive lattice smoothers

Conventional least squares order-recursive lattice (LSORL) filters use present and past data values to estimate the present value of a signal. This paper introduces LSORL smoothers which use past, present and future data for that purpose. Except for an overall delay needed for physical realization,...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1995-05, Vol.43 (5), p.1058-1067
Main Authors: Jenq-Tay Yuan, Stuller, J.A.
Format: Article
Language:English
Subjects:
Applied sciences
Delay estimation
Detection, estimation, filtering, equalization, prediction
Estimation error
Exact sciences and technology
Finite impulse response filter
Information, signal and communications theory
Kalman filters
Lattices
Least squares approximation
Least squares methods
Mean square error methods
Nonlinear filters
Signal and communications theory
Signal, noise
Telecommunications and information theory
Transversal filters
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Summary:Conventional least squares order-recursive lattice (LSORL) filters use present and past data values to estimate the present value of a signal. This paper introduces LSORL smoothers which use past, present and future data for that purpose. Except for an overall delay needed for physical realization, LSORL smoothers can substantially outperform LSORL filters while retaining all the advantages of an order-recursive structure.< >
ISSN:1053-587X
1941-0476
DOI:10.1109/78.382393