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Observations on linear estimation

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2 Author(s)
Jackson, L.B. ; University of Rhode Island, Rhode Island ; Soong, F.

Heisey and Griffiths have proposed a generalization of linear prediction, called "linear estimation", in which both past and future data samples are used to predict (estimate) the present sample. They report that although the mean-square error from this formulation is usually smaller than from standard linear prediction, the corresponding spectral estimate is a poorer fit to the true spectrum. We give a general explanation for this apparent paradox in terms of the zeros of the estimated inverse filter and examine specifically the case of frequency estimation for a single complex sinusoid in noise. The intuitively appealing idea that future as well as past data should be included in the estimates is best implemented by a combined forward-backward prediction method.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '78.  (Volume:3 )

Date of Conference:

Apr 1978