UNBIASED LEAST-SQUARES LATTICE.

David Carl Swanson, Frank W. Symons

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A modification to both the unnormalized and the normalized least-squares lattice algorithms is presented that produces unbiased estimates of the lattice parameters without a significant increase in algorithm complexity. Unbiased parameter estimation is very useful for improving the numerical precision of the least-squares lattice algorithm because the parameters representing statistical estimates remain essentially constant in magnitude for stationary input data. In the unnormalized algorithm the large magnitudes of the cross-correlation and covariance parameters are avoided, while in the normalized algorithm the decreasing magnitudes of the error signals are kept at unity variance (not less than unity) through appropriate scaling of the lattice recursions. Both unbiased algorithms require an additional integer parameter representing the number of data observations used in the parameter estimates at each stage.

Original languageEnglish (US)
Pages (from-to)1189-1192
Number of pages4
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 1985

Fingerprint

unity
estimates
error signals
Parameter estimation
cross correlation
Lattice constants
integers
lattice parameters
scaling

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics

Cite this

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title = "UNBIASED LEAST-SQUARES LATTICE.",
abstract = "A modification to both the unnormalized and the normalized least-squares lattice algorithms is presented that produces unbiased estimates of the lattice parameters without a significant increase in algorithm complexity. Unbiased parameter estimation is very useful for improving the numerical precision of the least-squares lattice algorithm because the parameters representing statistical estimates remain essentially constant in magnitude for stationary input data. In the unnormalized algorithm the large magnitudes of the cross-correlation and covariance parameters are avoided, while in the normalized algorithm the decreasing magnitudes of the error signals are kept at unity variance (not less than unity) through appropriate scaling of the lattice recursions. Both unbiased algorithms require an additional integer parameter representing the number of data observations used in the parameter estimates at each stage.",
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UNBIASED LEAST-SQUARES LATTICE. / Swanson, David Carl; Symons, Frank W.

In: Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, 1985, p. 1189-1192.

Research output: Contribution to journalArticle

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T1 - UNBIASED LEAST-SQUARES LATTICE.

AU - Swanson, David Carl

AU - Symons, Frank W.

PY - 1985

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