### Abstract

The problem discussed in this paper is that of estimating the period of a sequence of periodic events when the measurements of the occurrence times are noisy and sparse. The problem is common to many signal processing applications, such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Previous algorithms have been based on periodogram maximisation [1, 2], Euclidean algorithms [3-5], least-squares line search [6], lattice line search [7] and Gaussian maximum likelihood [8]. Until now, very little has been known about the asymptotic statistical properties of any such algorithm. In this paper, a new algorithm is proposed, based on a modified least-squares approach. Under very general properties, the estimators of the system parameters are shown to have excellent (theoretical) asymptotic statistical properties. These properties are illustrated using a number of simulations.

Language | English |
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Title of host publication | 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 |

Place of Publication | Piscataway, N.J. |

Publisher | Institute of Electrical and Electronics Engineers (IEEE) |

Pages | 193-196 |

Number of pages | 4 |

ISBN (Print) | 9781467301831 |

DOIs | |

Publication status | Published - 2012 |

Event | 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States Duration: 5 Aug 2012 → 8 Aug 2012 |

### Other

Other | 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 |
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Country | United States |

City | Ann Arbor, MI |

Period | 5/08/12 → 8/08/12 |

### Fingerprint

### Cite this

*2012 IEEE Statistical Signal Processing Workshop, SSP 2012*(pp. 193-196). [6319657] Piscataway, N.J.: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SSP.2012.6319657

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*2012 IEEE Statistical Signal Processing Workshop, SSP 2012.*, 6319657, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, N.J., pp. 193-196, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, Ann Arbor, MI, United States, 5/08/12. https://doi.org/10.1109/SSP.2012.6319657

**Estimating period from sparse, noisy timing data.** / Quinn, Barry G.; Clarkson, I. Vaughan L; McKilliam, Robby G.

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

TY - GEN

T1 - Estimating period from sparse, noisy timing data

AU - Quinn, Barry G.

AU - Clarkson, I. Vaughan L

AU - McKilliam, Robby G.

PY - 2012

Y1 - 2012

N2 - The problem discussed in this paper is that of estimating the period of a sequence of periodic events when the measurements of the occurrence times are noisy and sparse. The problem is common to many signal processing applications, such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Previous algorithms have been based on periodogram maximisation [1, 2], Euclidean algorithms [3-5], least-squares line search [6], lattice line search [7] and Gaussian maximum likelihood [8]. Until now, very little has been known about the asymptotic statistical properties of any such algorithm. In this paper, a new algorithm is proposed, based on a modified least-squares approach. Under very general properties, the estimators of the system parameters are shown to have excellent (theoretical) asymptotic statistical properties. These properties are illustrated using a number of simulations.

AB - The problem discussed in this paper is that of estimating the period of a sequence of periodic events when the measurements of the occurrence times are noisy and sparse. The problem is common to many signal processing applications, such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Previous algorithms have been based on periodogram maximisation [1, 2], Euclidean algorithms [3-5], least-squares line search [6], lattice line search [7] and Gaussian maximum likelihood [8]. Until now, very little has been known about the asymptotic statistical properties of any such algorithm. In this paper, a new algorithm is proposed, based on a modified least-squares approach. Under very general properties, the estimators of the system parameters are shown to have excellent (theoretical) asymptotic statistical properties. These properties are illustrated using a number of simulations.

UR - http://www.scopus.com/inward/record.url?scp=84868249822&partnerID=8YFLogxK

U2 - 10.1109/SSP.2012.6319657

DO - 10.1109/SSP.2012.6319657

M3 - Conference proceeding contribution

SN - 9781467301831

SP - 193

EP - 196

BT - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, N.J.

ER -