A modified Least Squares Lattice filter to identify non stationary process

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dc.creatorCuoco, Elena
dc.date2002-11-18
dc.date.accessioned2026-07-07T05:48:27Z
dc.date.available2026-07-07T05:48:27Z
dc.descriptionIn this paper the author proposes to use the Least Squares Lattice filter with forgetting factor to estimate time-varying parameters of the model for noise processes. We simulated an Auto-Regressive (AR) noise process in which we let the parameters of the AR vary in time. We investigate a new way of implementation of Least Squares Lattice filter in following the non stationary time series for stochastic process. Moreover we introduce a modified Least Squares Lattice filter to whiten the time-series and to remove the non stationarity. We apply this algorithm to the identification of real times series data produced by recorded voice.
dc.description19 pages, 15 figures, uses elsart.cls submitted to Signal Processing
dc.identifierhttps://arxiv.org/abs/physics/0211077
dc.identifierhttp://arxiv.org/abs/physics/0211077
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/85029
dc.subjectData Analysis, Statistics and Probability
dc.subjectInstrumentation and Detectors
dc.titleA modified Least Squares Lattice filter to identify non stationary process
dc.typetext

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