Unbiased parameter estimation of nonstationary signals in noise
Recent approaches to the modeling of nonstationary signals by means of AR or ARMA models use a representation with time-varying parameters. The time-varying parameters are assumed to be linear combinations of a set of basis time functions so that the model is specified by constant parameters. For st...
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Veröffentlicht in: | IEEE transactions on acoustics, speech, and signal processing speech, and signal processing, 1986-10, Vol.34 (5), p.1319-1322 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Recent approaches to the modeling of nonstationary signals by means of AR or ARMA models use a representation with time-varying parameters. The time-varying parameters are assumed to be linear combinations of a set of basis time functions so that the model is specified by constant parameters. For stationary signals disturbed by white noise, an approach based upon a modified least-squares method leads to a good unbiased estimator of the parameters. In this correspondence, a similar algorithm deriving the unbiased parameters for nonstationary signals in white noise is given. The experimental results show the good performance of the proposed estimator. |
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ISSN: | 0096-3518 |
DOI: | 10.1109/TASSP.1986.1164926 |