A  kepstrum  approach  to  filtering, smoothing  and prediction

dc.contributor.author	Moir, T.J.
dc.contributor.author	Barrett, J.F.
dc.date.accessioned	2013-05-07T04:34:48Z
dc.date.available	2013-05-07T04:34:48Z
dc.date.issued	2002
dc.description.abstract	The kepstrum (or complex cepstrum) method is revisited and applied to the problem of spectral factorization where the spectrum is directly estimated from observations. The solution to this problem in turn leads to a new approach to optimal filtering, smoothing and prediction using the Wiener theory. Unlike previous approaches to adaptive and self-tuning filtering, the technique, when implemented, does not require a priori information on the type or order of the signal generating model. And unlike other approaches - with the exception of spectral subtraction - no state-space or polynomial model is necessary. In this first paper results are restricted to stationary signal and additive white noise.	en
dc.identifier.citation	Moir, T.J., Barrett, J.F. (2002), A kepstrum approach to filtering, smoothing and prediction, Research Letters in the Information and Mathematical Sciences, 3, 135-147	en
dc.identifier.issn	1175-2777
dc.identifier.uri	http://hdl.handle.net/10179/4351
dc.language.iso	en	en
dc.publisher	Massey University	en
dc.title	A kepstrum approach to filtering, smoothing and prediction	en
dc.type	Article	en

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