Reformulation of Pisarenko Harmonic Decomposition Method for Single-Tone Frequency Estimation

H. C. So; K. W. Chan

doi:10.1109/TSP.2004.823473

Reformulation of Pisarenko Harmonic Decomposition Method for Single-Tone Frequency Estimation

H. C. So, K. W. Chan

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

76 Citations (Scopus)

Abstract

Based on the linear prediction (LP) property of sinusoidal signals, a closed-form unbiased frequency estimator for a real sinusoid in white noise is proposed. The frequency estimator, which is derived by minimizing a constrained least squares cost function, can be considered as a reformulation of the well known Pisarenko harmonic decomposer (PHD). Online computation of the frequency estimate can be achieved in a very simple manner, and its variance is derived. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with the PHD, maximum likelihood, and LP-based methods as well as Cramér-Rao lower bound.

Original language	English
Pages (from-to)	1128-1135
Journal	IEEE Transactions on Signal Processing
Volume	52
Issue number	4
DOIs	https://doi.org/10.1109/TSP.2004.823473
Publication status	Published - Apr 2004

Research Keywords

Constrained optimization
Frequency estimation
Low complexity
Online algorithm
Pisarenko's method
Real sinusoid

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title = "Reformulation of Pisarenko Harmonic Decomposition Method for Single-Tone Frequency Estimation",

abstract = "Based on the linear prediction (LP) property of sinusoidal signals, a closed-form unbiased frequency estimator for a real sinusoid in white noise is proposed. The frequency estimator, which is derived by minimizing a constrained least squares cost function, can be considered as a reformulation of the well known Pisarenko harmonic decomposer (PHD). Online computation of the frequency estimate can be achieved in a very simple manner, and its variance is derived. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with the PHD, maximum likelihood, and LP-based methods as well as Cram{\'e}r-Rao lower bound.",

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AU - So, H. C.

AU - Chan, K. W.

PY - 2004/4

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N2 - Based on the linear prediction (LP) property of sinusoidal signals, a closed-form unbiased frequency estimator for a real sinusoid in white noise is proposed. The frequency estimator, which is derived by minimizing a constrained least squares cost function, can be considered as a reformulation of the well known Pisarenko harmonic decomposer (PHD). Online computation of the frequency estimate can be achieved in a very simple manner, and its variance is derived. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with the PHD, maximum likelihood, and LP-based methods as well as Cramér-Rao lower bound.

AB - Based on the linear prediction (LP) property of sinusoidal signals, a closed-form unbiased frequency estimator for a real sinusoid in white noise is proposed. The frequency estimator, which is derived by minimizing a constrained least squares cost function, can be considered as a reformulation of the well known Pisarenko harmonic decomposer (PHD). Online computation of the frequency estimate can be achieved in a very simple manner, and its variance is derived. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with the PHD, maximum likelihood, and LP-based methods as well as Cramér-Rao lower bound.

KW - Constrained optimization

KW - Frequency estimation

KW - Low complexity

KW - Online algorithm

KW - Pisarenko's method

KW - Real sinusoid

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DO - 10.1109/TSP.2004.823473

M3 - RGC 21 - Publication in refereed journal

SN - 1053-587X

VL - 52

SP - 1128

EP - 1135

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 4

ER -

Reformulation of Pisarenko Harmonic Decomposition Method for Single-Tone Frequency Estimation

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Research Keywords

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