Convergence Properties of an Adaptive Digital Lattice Filter

Michael L. Honig, David G. Messerschmitt

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

Convergence properties of a continuously adaptive digital lattice filter used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean values and the output mean-square error (MSE) are approximated and a simple model is described which approximates these quantifies as functions of time. Calculated curves using this model are compared with simulation results. Results obtained for a two-stage lattice are then compared with the two-stage least mean-square (LMS) transversal filter algorithm, demonstrating that it is possible but unlikely for the transversal filter to converge faster than the analogous lattice filter.

Original languageEnglish (US)
Pages (from-to)642-653
Number of pages12
JournalIEEE Transactions on Acoustics, Speech, and Signal Processing
Volume29
Issue number3
DOIs
StatePublished - Jan 1 1981

ASJC Scopus subject areas

  • Signal Processing

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