### Abstract

The measurement of the spectrum of periodic signals is generally done by assuming zero variance. However, since the phase is often random, this is not exactly true, though generally the variance is small indeed. In the paper expressions of the variance of the periodogram-based spectral estimator are derived, in the case of different windows, for periodic and for mixed periodic-Gaussian signals. Some examples are given for the cases, when the variance is significant.

Original language | English |
---|---|

Title of host publication | Unknown Host Publication Title |

Publisher | Cleup Editore |

Pages | 87-91 |

Number of pages | 5 |

Publication status | Published - 1984 |

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Unknown Host Publication Title*(pp. 87-91). Cleup Editore.

**VARIANCE OF THE POWER DENSITY SPECTRUM WHEN MEASURING PERIODIC SIGNALS.** / Kollár, I.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Unknown Host Publication Title.*Cleup Editore, pp. 87-91.

}

TY - GEN

T1 - VARIANCE OF THE POWER DENSITY SPECTRUM WHEN MEASURING PERIODIC SIGNALS.

AU - Kollár, I.

PY - 1984

Y1 - 1984

N2 - The measurement of the spectrum of periodic signals is generally done by assuming zero variance. However, since the phase is often random, this is not exactly true, though generally the variance is small indeed. In the paper expressions of the variance of the periodogram-based spectral estimator are derived, in the case of different windows, for periodic and for mixed periodic-Gaussian signals. Some examples are given for the cases, when the variance is significant.

AB - The measurement of the spectrum of periodic signals is generally done by assuming zero variance. However, since the phase is often random, this is not exactly true, though generally the variance is small indeed. In the paper expressions of the variance of the periodogram-based spectral estimator are derived, in the case of different windows, for periodic and for mixed periodic-Gaussian signals. Some examples are given for the cases, when the variance is significant.

UR - http://www.scopus.com/inward/record.url?scp=0021541227&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0021541227&partnerID=8YFLogxK

M3 - Conference contribution

SP - 87

EP - 91

BT - Unknown Host Publication Title

PB - Cleup Editore

ER -