### 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 language | English (US) |
---|---|

Pages (from-to) | 1189-1192 |

Number of pages | 4 |

Journal | Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing |

State | Published - 1985 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

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

### Cite this

*Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing*, 1189-1192.

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*Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing*, pp. 1189-1192.

**UNBIASED LEAST-SQUARES LATTICE.** / Swanson, David Carl; Symons, Frank W.

Research output: Contribution to journal › Article

TY - JOUR

T1 - UNBIASED LEAST-SQUARES LATTICE.

AU - Swanson, David Carl

AU - Symons, Frank W.

PY - 1985

Y1 - 1985

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0022197064&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0022197064&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0022197064

SP - 1189

EP - 1192

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

ER -