Convergence Models for Lattice Joint Process Estimators and Least Squares Algorithms

Michael L. Honig*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

A simple model characterizing the convergence properties of an adaptive digital lattice Filter using gradient algorithms has been reported [1]. This model is extended to the least mean square (LMS) lattice joint process estimator [5], and to the least squares (LS) lattice and “fast” Kalman algorithms [9] -[16]. The models in each case are compared with computer simulation. The single-stage LMS lattice analysis presented in [1] is also applied to the LS lattice. Results indicate that for stationary inputs, the LMS lattice and LS algorithms exhibit similar behavior.

Original languageEnglish (US)
Pages (from-to)415-425
Number of pages11
JournalIEEE Transactions on Acoustics, Speech, and Signal Processing
Volume31
Issue number2
DOIs
StatePublished - Apr 1983

ASJC Scopus subject areas

  • Signal Processing

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